The standard firefly algorithm.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"5241",leadTitle:null,fullTitle:"Applications of Spatial Statistics",title:"Applications of Spatial Statistics",subtitle:null,reviewType:"peer-reviewed",abstract:"Spatial statistics has been widely used in many environmental studies. This book is a collection of recent studies on applying spatial statistics in subjects such as demography, transportation, precision agriculture and ecology. Different subjects require different aspects of spatial statistics. In addition to quantitative statements from statistics and tests, visualization in forms of maps, drawings, and images are provided to illustrate the relationship between data and locations. This book will be valuable to researchers who are interested in applying statistics to spatial data, as well as graduate students who know statistics and want to explore how it can be applied to spatial data. With the processing part being simplified to several mouse clicks by commercial software, one should pay more attention to justification of using spatial statistics, as well as interpretation and assessment of the results. GIScience proves to be a useful tool in visualization of spatial data, and such useful technology should be utilized, as part, for the interpretation and assessment of the results.",isbn:"978-953-51-2757-4",printIsbn:"978-953-51-2756-7",pdfIsbn:"978-953-51-5085-5",doi:"10.5772/61666",price:119,priceEur:129,priceUsd:155,slug:"applications-of-spatial-statistics",numberOfPages:156,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"acc5941907640ecc7a3e350c5fe3df19",bookSignature:"Ming-Chih Hung",publishedDate:"November 2nd 2016",coverURL:"https://cdn.intechopen.com/books/images_new/5241.jpg",numberOfDownloads:13363,numberOfWosCitations:14,numberOfCrossrefCitations:17,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:31,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:62,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 28th 2015",dateEndSecondStepPublish:"November 18th 2015",dateEndThirdStepPublish:"March 31st 2016",dateEndFourthStepPublish:"April 30th 2016",dateEndFifthStepPublish:"June 30th 2016",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"184413",title:"Dr.",name:"Ming",middleName:"Chih",surname:"Hung",slug:"ming-hung",fullName:"Ming Hung",profilePictureURL:"https://mts.intechopen.com/storage/users/184413/images/4908_n.jpg",biography:"Ming-Chih Hung is a Professor of Geography/GIScience at Northwest Missouri State University. He earned his bachelor degree from National Taiwan University focusing on DEM applications, his MS from the University of Utah focusing on the V-I-S Model and soft classification of TM images, and his Ph.D. also from the University of Utah focusing on remote sensing and GIS on urban areas. His research emphasizes on use of GIScience on urban areas. In addition to urban areas, he is also interested in precision agriculture and environmental issues. At Northwest Missouri State, he teaches courses on remote sensing, GIS, GPS, and cartography.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Northwest Missouri State University",institutionURL:null,country:{name:"United States of America"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"637",title:"Spatial Analysis",slug:"spatial-analysis"}],chapters:[{id:"52302",title:"Application of Spatial Statistics in Transportation Engineering",doi:"10.5772/65051",slug:"application-of-spatial-statistics-in-transportation-engineering",totalDownloads:2450,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"“Everything is related to everything else, but near things are more related than distant things” is the first law of geography. It can be hypothesized that spatially, occurrence of a crash can exhibit similarities. To identify spatial patterns of crashes, this chapter presents spatial autocorrelation techniques such as Moran’s I and the Getis-Ord Gi*statistics; spatial interpolation such as kriging; and nonparametric probability density function and kernel density (K). The aim of this chapter is to provide application of spatial statistics in transportation engineering specifically to identify crash concentrations and patterns of clusters in a study area.",signatures:"Uday R.R. Manepalli and Ghulam H. Bham",downloadPdfUrl:"/chapter/pdf-download/52302",previewPdfUrl:"/chapter/pdf-preview/52302",authors:[{id:"182804",title:"Dr.",name:"Ghulam H.",surname:"Bham",slug:"ghulam-h.-bham",fullName:"Ghulam H. Bham"},{id:"188145",title:"Mr.",name:"Uday",surname:"Manepalli",slug:"uday-manepalli",fullName:"Uday Manepalli"}],corrections:null},{id:"52704",title:"Comparison of Spatial Interpolation Techniques Using Visualization and Quantitative Assessment",doi:"10.5772/65996",slug:"comparison-of-spatial-interpolation-techniques-using-visualization-and-quantitative-assessment",totalDownloads:3774,totalCrossrefCites:12,totalDimensionsCites:25,hasAltmetrics:0,abstract:"Spatial interpolation has been widely and commonly used in many studies to create surface data based on a set of sampled points, such as soil properties, temperature, and precipitation. Currently, there are many commercial Geographic Information System (GIS) or statistics software offering spatial interpolation functions, such as inverse distance weighted (IDW), kriging, spline, and others. To date, there is no “rule of thumb” on the most appropriate spatial interpolation techniques for certain situations, though general suggestions have been published. Many studies rely on quantitative assessment to determine the performance of spatial interpolation techniques. Most quantitative assessment methods provide a numeric index for the overall performance of an interpolated surface. Although it is objective and convenient, there are many facts or trends not captured by quantitative assessments. This study used 2D visualization and 3D visualization to identify trends not evident in quantitative assessment. This study also presented a special case, a closed system in which all interpolated surfaces should sum up to 100%, to demonstrate the interaction between interpolated surfaces that were created separately and independently.",signatures:"Yi-Hwa (Eva) Wu and Ming-Chih Hung",downloadPdfUrl:"/chapter/pdf-download/52704",previewPdfUrl:"/chapter/pdf-preview/52704",authors:[{id:"181853",title:"Dr.",name:"Yi-Hwa",surname:"Wu",slug:"yi-hwa-wu",fullName:"Yi-Hwa Wu"}],corrections:null},{id:"51744",title:"Wage Concentration in Spain: A Spatial Analysis",doi:"10.5772/64441",slug:"wage-concentration-in-spain-a-spatial-analysis",totalDownloads:1582,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this article, the degree of concentration of wages in Spain at the provincial and regional levels is estimated using the latest available micro-data corresponding to the Structure of Earnings Survey 2010 (N = 216.769). From the analysis of the statistics obtained, it is possible to know in detail the spatial distribution of national wage inequality, to identify those areas where inequality is greatest, and to estimate the possible existence of spatial dependence and structure. The analysis focuses not only on the study of global inequality, but delves into the question by extending the analysis from a gender perspective.",signatures:"Beatriz Larraz, Mónica Navarrete and José Manuel Pavía",downloadPdfUrl:"/chapter/pdf-download/51744",previewPdfUrl:"/chapter/pdf-preview/51744",authors:[{id:"183051",title:"Dr.",name:"Beatriz",surname:"Larraz",slug:"beatriz-larraz",fullName:"Beatriz Larraz"},{id:"183056",title:"Prof.",name:"Jose Manuel",surname:"Pavia",slug:"jose-manuel-pavia",fullName:"Jose Manuel Pavia"},{id:"183460",title:"Dr.",name:"Monica",surname:"Navarrete",slug:"monica-navarrete",fullName:"Monica Navarrete"}],corrections:null},{id:"51918",title:"Spatial Optimization of Urban Cellular Automata Model",doi:"10.5772/64788",slug:"spatial-optimization-of-urban-cellular-automata-model",totalDownloads:1947,totalCrossrefCites:3,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Although cellular automata (CA) offer a modelling framework and set of techniques for modelling the dynamic processes of urban growth, determining the optimal value of weights or parameters for elements or factors of urban CA models is challenging. This chapter demonstrates the implementation of a calibration module in a fuzzy cellular urban growth model (FCUGM) for optimizing the weights and parameters of an urban CA model using three types of algorithms: (i) genetic algorithm (GA), (ii) parallel simulated annealing (PSA) and (iii) expert knowledge (EK). It was found that the GA followed by EK produced better and more accurate and consistent results compared with PSA. This suggests that the GA was able to some extent to understand the urban growth process and the underlying relationship between input factors in a way similar to human experts. It also suggests that the two algorithms (GA and EK) have similar agreement about the efficiency of scenarios in terms of modelling urban growth. In contrast, the results of the PSA do not show results corresponding to those of the GA or EK. This suggests that the complexity of the urban process is beyond the algorithm’s capability or could be due to being trapped in local optima. With this satisfactory calibration of the FCUGM for the urban growth of Riyadh city in Saudi Arabia by using CALIB-FCUGM, these calibrated parameters can be passed into the SIM-FCUGM to simulate the spatial patterns of urban growth of Riyadh.",signatures:"Khalid Al-Ahmadi, Mohammed Alahmadi and Sabah Alahmadi",downloadPdfUrl:"/chapter/pdf-download/51918",previewPdfUrl:"/chapter/pdf-preview/51918",authors:[{id:"137051",title:"Dr.",name:"Khalid",surname:"Al-Ahmadi",slug:"khalid-al-ahmadi",fullName:"Khalid Al-Ahmadi"}],corrections:null},{id:"52451",title:"Structural Diversity of Plant Populations: Insight from Spatial Analyses",doi:"10.5772/65320",slug:"structural-diversity-of-plant-populations-insight-from-spatial-analyses",totalDownloads:1855,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Spatial analysis has been one of the most rapidly growing fields in ecology. It is related directly to a growing awareness among researchers that a spatial structure of biosystems, e.g., forests, is important in ecological thinking. The availability of the specific software supports the use of spatial analyses in different fields of the science and forestry science is only one example for this. Many data collected in the forests have the spatial and temporal dimensions and it allows us to use spatial statistics to quantitative description of the spatial structure of forest, which became an important element of modern continuous cover forestry. In this chapter, key elements: data types, null models, and summary statistics, which can be applied in spatial analyses, are briefly described. Real data sets collected from different forests were given to provide examples of spatial analyses. The key elements of spatial analysis in ecology are data type, the appropriate choice of summary statistics and null models. Selecting few of them in a single analysis makes the statements more reliable and realistic in the changing world.",signatures:"Janusz Szmyt",downloadPdfUrl:"/chapter/pdf-download/52451",previewPdfUrl:"/chapter/pdf-preview/52451",authors:[{id:"180608",title:"Dr.",name:"Janusz",surname:"Szmyt",slug:"janusz-szmyt",fullName:"Janusz Szmyt"}],corrections:null},{id:"52429",title:"Practical Value of User‐Centred Spatial Statistics for Responsive Urban Planning",doi:"10.5772/65322",slug:"practical-value-of-user-centred-spatial-statistics-for-responsive-urban-planning",totalDownloads:1760,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This chapter addresses spatial statistics via an alternative perspective, focusing on evidence‐based people‐spatial relationships and related measures, quantifications and qualifications, and by this, it provides rather specific spatial information and spatial statistics about urban environments. It is based on time quality assessment (TQA), a time‐people‐place‐oriented approach for the analysis and simulation of the quality of living environments, backgrounded with the method of behaviour mapping. It shows that the quality of the time spent on a certain activity in a certain place indicates the quality of the living environment. It also shows that the quality of the time spent depends on what a person can afford, and it provides an evaluation of the quality of living environments with a measure of good/bad time. The practical value is in the provision of empirical knowledge to support planning guidance based on user‐centred small‐scale spatial statistics, which is able to inform top‐down and bottom‐up decision‐making processes for people‐friendly living environments.",signatures:"Damjan Marušić and Barbara Goličnik Marušić",downloadPdfUrl:"/chapter/pdf-download/52429",previewPdfUrl:"/chapter/pdf-preview/52429",authors:[{id:"104025",title:"Dr.",name:"Damjan",surname:"Marušić",slug:"damjan-marusic",fullName:"Damjan Marušić"},{id:"184731",title:"Dr.",name:"Barbara",surname:"Goličnik Marušić",slug:"barbara-golicnik-marusic",fullName:"Barbara Goličnik Marušić"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"6058",title:"Recent Advances and Applications in Remote Sensing",subtitle:null,isOpenForSubmission:!1,hash:"6c271ba0275d66a79d494e5e0bfe9ebb",slug:"recent-advances-and-applications-in-remote-sensing",bookSignature:"Ming-Chih Hung and Yi-Hwa Wu",coverURL:"https://cdn.intechopen.com/books/images_new/6058.jpg",editedByType:"Edited by",editors:[{id:"184413",title:"Dr.",name:"Ming",surname:"Hung",slug:"ming-hung",fullName:"Ming Hung"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7304",title:"Geospatial Analyses of Earth Observation (EO) data",subtitle:null,isOpenForSubmission:!1,hash:"e90c7cda0e7f94a6620d6ec83db808ae",slug:"geospatial-analyses-of-earth-observation-eo-data",bookSignature:"Antonio Pepe and Qing Zhao",coverURL:"https://cdn.intechopen.com/books/images_new/7304.jpg",editedByType:"Edited by",editors:[{id:"99269",title:"Dr.",name:"Antonio",surname:"Pepe",slug:"antonio-pepe",fullName:"Antonio Pepe"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7262",title:"Spatial Analysis, Modelling and Planning",subtitle:null,isOpenForSubmission:!1,hash:"ed7c7f4193e3951e715569ca454f7077",slug:"spatial-analysis-modelling-and-planning",bookSignature:"Jorge Rocha and José António Tenedório",coverURL:"https://cdn.intechopen.com/books/images_new/7262.jpg",editedByType:"Edited by",editors:[{id:"145918",title:"Ph.D.",name:"Jorge",surname:"Rocha",slug:"jorge-rocha",fullName:"Jorge Rocha"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9246",title:"Satellites Missions and Technologies for Geosciences",subtitle:null,isOpenForSubmission:!1,hash:"f23d04613b089dae40f81342c3e7c7f4",slug:"satellites-missions-and-technologies-for-geosciences",bookSignature:"Vladislav Demyanov and Jonathan Becedas",coverURL:"https://cdn.intechopen.com/books/images_new/9246.jpg",editedByType:"Edited by",editors:[{id:"154597",title:"Prof.",name:"Vladislav",surname:"Demyanov",slug:"vladislav-demyanov",fullName:"Vladislav Demyanov"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9846",title:"Spatial Variability in Environmental Science",subtitle:"Patterns, Processes, and Analyses",isOpenForSubmission:!1,hash:"cfa4fa7b982bbff46ffbe6fbdbffbdf1",slug:"spatial-variability-in-environmental-science-patterns-processes-and-analyses",bookSignature:"John P. 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The rise of Taylorism, standardisation, electrical systems, electronic systems and computing, and now, quantum computing, has given scheduling a whole World of importance.
\r\n\tFrom practice to a mathematical and technological application, scheduling has become another form of art: an algorithmic art, declined in as many OS and hardware constraints, from embedded systems onboard an aircraft or a spacecraft to databases in all financial and Internet servers.
\r\n\tThey have become ubiquitous so that a large part of our civilisational development is supported by their reliability, redundancy, and optimisation capacity. Like all of our civilisational assets, they are benefiting from scientific breakthrough in computational sciences such as evolutionary algorithms, Artificial Intelligence, and quantum computing. If not by using it, by being in need of adapting to the next generation of computing. Space development is also bringing new challenges, especially in redundancy and reliability.
An optimization problem refers to the maximization or minimization of an objective function by setting suitable values for the variables from a set of feasible values. These problems appear not only in complex scientific studies but also in our day-to-day activities. For instance, when a person wants to go from one place to another and has multiple possible routes, a decision needs to be made on which route to take. The decision can be with the objective to minimize travel time, fuel consumption and so on. However, these kinds of problems with fewer number of alternatives can easily be solved by looking at the outcome of each of the alternatives. However, in real problems, it is not always the case to have a finite and small number of alternatives. Hence, different solution methods are proposed based on the behaviour of the problem.
\nSince the introduction of evolutionary algorithms, many studies have been conducted on heuristic algorithms. Introducing new algorithms has been one of the leading research areas [1]. Currently, there are more than 40 metaheuristic algorithms [2]. Most of these new algorithms are introduced by mimicking a scenario from nature. For instance, genetic algorithm is inspired by the Darwin theory of survival of the fittest [3]; particle swarm optimization is another metaheuristic algorithm mimicking how a swarm moves by following each other [4]; firefly algorithm is inspired by how fireflies signal each other using the flashing light to attract for mating or to identify predators [5] and prey predator algorithm is another new algorithm inspired by the behaviour of a predator and its prey [6]. These algorithms use different degree of exploration and exploitation based on their different search mechanisms.
\nFirefly algorithm is among those metaheuristic algorithms which have different applications. Its uncomplicated and easy steps with its effectiveness attract researchers from different disciplines it. Different studies have been performed to modify the standard firefly algorithm to boost its performance and to make it suitable for a problem at hand. In this chapter, a comprehensive study will be presented on firefly algorithm and its modified versions. A brief discussion on extended firefly algorithm with other relevant studies will also be provided. In the next section, a discussion on optimization problems with their solution methods will be given followed by a review on studies on firefly algorithm, which includes a discussion on the standard firefly algorithm with its modified versions and other relevant studies on firefly algorithm, in Section 3. In Section 4, a comparative study based on simulation results will be presented followed by summary of the chapter in Section 5.
\nDecision-making problems can be found beyond our daily activity. They are very common in engineering, management and in many other disciplines. Different researchers used the concept of optimization in different applications, including engineering applications, transportation planning, management applications, economics, computational intelligence, decision science, agriculture, tourism, sport science and even political science [7–18].
\nWhen these problems are formulated mathematically, they are called mathematical optimization problems. It will have a set of feasible actions, also called feasible regions, and a measure of performance of these actions called the objective. A standard single objective minimization problem can be given as in Eq. (1).
where
In a broad sense, optimization solution methods can be categorized as exact and approximate solution methods. Exact solution methods are methods which use an exhaustive search for the exact solution in the solution space. They use mathematical and statistical arguments to get an exact solution. They mainly used calculus-based and iterative procedures. Perhaps Fermat is the first to use a calculus-based argument to solve optimization problems [19]. Iterative methods were first proposed and used by Newton and Gauss [20]. Since then, several exact solution methods are proposed and used in different problems. Branch and bound, simplex method and gradient descent method are good examples of exact solution methods. However, due to complex problems modelled from complex real aspects, it becomes challenging for the deterministic solution methods. This leads to the search of new \'out of the box\' way of solving these problems, which in turn gives rise to the birth of metaheuristic solution algorithms.
\nMetaheuristic algorithms are approximate solution methods for an optimization problem which use a randomness property with an \'educated guess\' in their search mechanism and try to improve the quality of the solutions at hand through the iterations, from a randomly generated set of feasible solutions, by exploring and exploiting the solution space. Even though these algorithms do not guaranty optimality, they are tested to give a reasonable and acceptable solution. Furthermore, they have the advantage of not to be affected much by the behaviour of the problem; this makes them useful in many applications. Having a variety of algorithms will give the option to choose a suitable one to solve a problem according to its behaviour.
\nNature has been an inspiration for the introduction of many metaheuristic algorithms. It has managed to find solution to problems without being told but through experience. Natural selection and survival of the fittest was the main motivation behind the early metaheuristic algorithms. Different animals communicate with each other through different mode of communications. Fireflies use their flashing property to communicate. There are around 2000 firefly species with their own distinct flash patterns. They usually produce a short flash with a certain pattern. The light is produced by a biochemical process called the bioluminescence. The flashing communication is used to attract their mate and also to warn predators. Based on the pattern of the light, a suitable mate will communicate back by either mimicking the same pattern or responding with a specific pattern. It also needs to be noted that the light intensity decreases through distance; hence, a flashing light emanating from a firefly gets a response from fireflies around it within a visual range of the flash.
\nIn addition to enjoying the beautiful view of a summer sky created by fireflies, they have motivated and have been the centre for many scientific researches [5, 21, 22]. In the sense of optimization, if we consider the fireflies as solution on the landscape of the solution space, then the attraction and movement of fireflies can inspire an optimization algorithm in which solutions follow better (brighter) solutions. Hence, firefly algorithm is motivated and inspired by these properties.
\nFirefly algorithm is a swarm-based metaheuristic algorithm which was introduced by Yang [5]. The algorithm mimics how fireflies interact using their flashing lights. The algorithm assumes that all fireflies are unisex, which means any firefly can be attracted by any other firefly; the attractiveness of a firefly is directly proportional to its brightness which depends on the objective function. A firefly will be attracted to a brighter firefly. Furthermore, the brightness decreases through distance based on inverse square law, as given in Eq. (2).
If the light is passing through a medium with a light absorption coefficient
where
A generalized brightness function for
In the algorithm, a randomly generated feasible solution, called fireflies, will be assigned with a light intensity based on their performance in the objective function. This intensity will be used to compute the brightness of the firefly, which is directly proportional to its light intensity. For minimization problems, a solution with smallest functional value will be assigned with highest light intensity. Once the intensity or brightness of the solutions is assigned, each firefly will follow fireflies with better light intensity. For the brightest firefly, it will perform a local search by randomly moving in its neighbourhood. Hence, for two fireflies, if firefly
where
The standard firefly algorithm.
These updates of the location of fireflies continue with iteration until a termination criterion is met. The termination criterion can be maximum number of iterations, a tolerance from the optimum value if it is known or no improvement is achieved in consecutive iterations. The algorithm is summarized in Table 1.
\nFirefly algorithm is efficient and an easy-to-implement algorithm. It is also suitable for parallel implementation. However, researches show that it is slow in convergence and easily gets trapped in local optimum for multimodal problems. In addition, the updates solely depend on current performance and no memory on previous best solutions and performances are kept. That may lead to losing better solutions. Furthermore, since the parameters are fixed, the search behaviour remains to be the same for any condition in all iterations. Hence modifying the standard firefly algorithm to boost its performance has been one of the research issues. Furthermore, the standard firefly algorithm is designed for continuous optimization problems; hence in order to use it for non-continuous problems it needs to be modified and adjusted.
\nBasically, there are three classes of modification. Class 1 modification is the modification on the parameters. It is the first category in which the parameters of the algorithm are modified and the same updating mechanisms or formulas are used. Class 2 contains new updating mechanisms. It includes modifications which change part or all of the updating formulas, add mutation operator and the likes. The last category, Class 3, includes modifications on the search space, perhaps with the same updating mechanism it may be easier to switch to another ‘easy-to-search’ space, and changes in the probability distribution when generating random numbers. The categories are not necessarily disjoint as some of the modifications may fall in multiple classes.
\nIn the standard firefly algorithm, the parameters in Eq. (6) are user-defined constants. Like any other metaheuristic algorithms, the performance of a firefly algorithm depends on these parameter values. They control the degree of exploration and exploitation.
\nSome of the modifications of firefly algorithm are done by making these parameters variable and adaptive. In recent researches on the modification of firefly algorithms, the parameters
To deal with parameter identification of infinite impulse response (IIR) and nonlinear systems, firefly algorithm is modified in [23]. The modification with the random movement is based on initial and final step lengths
Another firefly algorithm with adaptive
In [27], the randomized parameter is modified based on the number of iterations using
In extending firefly algorithm for multiobjective problems, an adaptive
Self-adaptive step firefly algorithm is another modification done to the third term of the updating process by Yu et al. [29]. The step length
Another study of modification of the random movement parameter based on the historic performance of the solution is presented in [30]. Based on its best position until current iteration,
The attraction of one firefly by another depends on the light intensity at the source of the brighter firefly as well as on the distance between them and the light absorption coefficient of the medium.
\nFor a small change in the distance between two fireflies results in a quick decrease of the attraction term. To deal with this problem, Lin et al. [31] introduced a virtual distance which will put
Tilahun and Ong [32] suggested that, rather than making
Due to the non-repetition and ergodicity of chaos, it can carry out overall searches at higher speeds. Hence, Gandomi et al. [33] proposed a modification on
Another modification in this category is done in [39]. In this chapter,
To overcome this challenge arising with an increase in the problem dimension and the size of the feasible region, Yan et al. [41] proposed a modification for the standard firefly algorithm. This modification is done on the generalized distance term given in Eq. (5), in which
In order to solve economic dispatch problem, firefly algorithm is modified in [42]. To increase the exploration property, the authors replaced the Cartesian distance by the minimum variation distance. In addition, they used mutation operator on
To deal with premature convergence, firefly algorithm has also been modified based on the light intensity [43]. The light intensity difference is defined by
For the optimal sizing and siting of voltage-controlled distribution generator in distributed network, firefly algorithm is modified and is used in [44]. The problem is to minimize the power loss by selecting optimal location for distributed generations and the power produced. In the modification
Another modification of the standard firefly algorithm to be listed in this category is done in [45]. The randomized parameter
For path planning of autonomous underwater vehicle, the parameters
A similar approach in which the parameters
In [49],
The updating mechanism in the standard firefly algorithm is guided by Eqs. (6) and (7). In Class 1 modification, the same updating equations are used but with adaptive preference. Class 2 modifications include modification on the updating equations including modification in the updating process of the best (the brightest) and the worst (the dimmer) solutions changing part of the updating equations and some modification with additional mutation operator.
\nIn a high dimensional problem, the exploration is weak which results in premature convergence. To deal with this, two modifications are proposed in [51] for the standard firefly algorithm. That is, for the initial random
for
for
if [
end for
\nend for
\nSimilar to the previous modification, here also the best solution will improve or will not change in each of the iterations.
\nOpposition-based learning is also used in [52], to update the dimmer solution
Indeed, it relocates the worst solution to a new position that may give the algorithm a good explorative behaviour.
\nJumper firefly algorithm is a modified firefly algorithm in which a memory on the performance of each of the solution is kept [53]. A criterion called hazard condition is defined, and solutions will be tested based on their previous performance. If they are in hazardous condition, they will be randomly replaced by a new solution. Hence, based on the hazard condition, a mutation can be done by replacing the weak solution based on previous performance by a new solution.
\nAnother modification in this category is done by Kazemzadeh-Parsi [54], where each iteration
Another modification of firefly algorithm by introducing new solutions as a mutation or crossover is given in [26, 56]. In addition to adaptive parameter
This is another category of Class 2 modification in which the updating formula, given by Eqs. (6) and (7), is modified or changed. The first modification, to mention in this category, is the modification proposed in [57]. For a firefly
A similar modification in the vicinity of the brighter firefly is given in [58]. They proposed two updating formulas, with and without division, as the authors name them. The updating formula, without division, is given by
For a data clustering problem, the standard firefly algorithm is modified firefly algorithm [60, 61]. They proposed a new updating formula to increase the influence of the brightest firefly. The new updating formula is given by
Fuzzy firefly algorithm is another modification of the standard firefly algorithm [63]. Even though they start with a wrong claim by saying "
Another modification with a new updating formula is proposed in [64] and is given by
Diversity-guided firefly algorithm is one of the recent modified versions [65]. The modification is done to make the solutions as diverse as possible with a given threshold. The updating mechanism of the standard firefly is used until diversity of the solution falls beyond the given threshold. The diversity is measured by
In [66, 67], a mutated firefly algorithm is proposed in such a way that the brighter firefly donates some of its features based on a new algorithm parameter called probability of mutation,
In [68], a firefly located at
Another modification in this category is introduced to deal with economic dispatch optimization of thermal units [69]. A memory is used to record the best solution found so far. Based on cultured differential evolution, the updating formula is modified as
Another modification in this category is presented in [70]. The updating formula becomes
This class of modifications is on the abstract level modification and includes two types of modifications. The first one is changing the solution space to an easy search space, and the second one is on the types of probability distribution that is used to generate a random vector direction for the random movement.
\nIn the modified version presented in [71], each component of a solution will be represented by quaternion
Perhaps the first work which tries to adapt the randomness movement in the updating process is by Farahani et al. [72, 73]. Even though they started with a wrong claim by saying ‘
By enhancing the random movement of a firefly algorithm, Levy firefly algorithm is introduced in [75]. This is the first modification made to firefly algorithm with the Levy distribution guiding the random movement by generating a random direction as well as the step length. The update formula is modified as
Even though firefly algorithm is introduced for continuous problems, due to its effectiveness it has been modified for non-continuous problems as well. In this section, we will look at three classes of modification. The first one is when modifications are made to solve binary problems. The second is for integer-valued problems which include problems whose variable can have discrete values. The last one is mixed problems in which some of the variables are continuous and the rest are non-continuous.
\nTo deal with set covering problem, a binary firefly algorithm is proposed in [82]. There is no modification in the updating process except converting the solution to either one or zero. Three ways of conversion are proposed in [82]. The conversion works dimension wise. After a solution
Another modification for binary problems which works dimension wise, in each dimension, is presented in [83]. The update formula of the standard firefly algorithm is used. After the update, the solution will lie in the interval [0, 1] using
Another discrete firefly algorithm, in order to deal with job, schedule problem is proposed in [89]. Each firefly
In [90], for a dynamic knapsack problem, firefly algorithm has been modified. The conversion of the solutions is done based on the property of the problem using priority-based encoding. In addition to making the algorithm to suit for the problem, some modifications are done to increase its effectiveness. One of the modifications is that a firefly
Another modification in this category is presented in [92]. In addition to the discretization, they have made
In [93], firefly algorithm has been modified to deal with software modularization as a graph-partitioning problem. Initially, random integer-encoded solutions are generated. The hamming distance, the number of different entries between two solutions with the same index, is used to measure the distance between two solutions. The update is done by switching a number of entries of a firefly by the entries from a brighter firefly.
\nAnother modification in this category is done in [94]. The modification is based on a concept of random key, which is proposed in [95]. The method uses a mapping of a random number space, [0,1]
In [96, 97], the standard firefly algorithm is modified for loading pattern enhancement. The generation of random solutions uses random permutation, and the distance between fireflies,
For travel salesman problem, firefly algorithm has been modified in [100]. Initial solutions are generated using permutation of
Another modification in this category is proposed in [101]. The decision variables,
Firefly algorithm has been discretized for supply selection problem in [102]. The sum of the absolute differences between the entries is used to measure the distance
Perhaps the first modification to the standard firefly algorithm in this category is presented in [103]. The updating of solutions is conducted using the updating mechanism of the standard firefly algorithm. To deal with the discrete variables, constraint handling mechanism is used based on penalty function. In addition, the authors proposed two ways to generate a diverse set of random initial solutions. An adaptive random step length is also proposed using similar updating way in [104]. The same approach is improved in [105] by adding a scaling parameter for the random movement based on the difference between the maximum and minimum values for each variable. Portfolio optimization can be expressed as a mean-variance problem which belongs to the group of quadratic mixed-integer programming problems. In [106, 107], firefly algorithm has been extended with the use of rounding function and constraint handling approach. Deb’s method [108] is also used for constraint handling. In addition,
Like any metaheuristic algorithm, firefly algorithm is prone to parameter values. It is noticed that changing the parameters based on the search state is effective. Hence, modification on parameters is a direct forward idea to improve the performance of firefly algorithm. As the search proceeds, in order to have a conversion with good precision, the randomness movement must decrease. Hence, the randomness step length,
With initial and final values of 2.5 and 0.4;
The decreasing scenario for
The attraction term has also been modified in different ways. Adaptive light absorption constant of the medium changing with iteration is given in some studies. This modifications use increasing function [46], decreasing function [43, 67] or neither increasing nor decreasing function [33–38, 49, 50] of
Hence,
The effect of chaotic map update of
The movement of the best solution should be tuned properly. If it is allowed to decrease then its best performance may get lost. Hence, the approaches used to preserve the best solution are ended effectively [32, 52].
\nMutation is another good approach to diversify the solutions which in turn increase the exploration behaviour of the algorithm [24, 53–58]. However, generating many solutions may hinder the search as it will take long to run. In addition, accepting weak solution should also be incorporated in deceiving problems; a solution needs to decrease in order to escape local solutions.
\nModifying the update equation is another interesting modification featured in some studies [56, 58–61, 63–67, 69]. These studies suggest that the update should be done in the vicinity of the brighter firefly [58, 60]. This is not always a good idea as the region in between the two solutions will not be explored. Some of the studies indicate an increase in the attraction towards brighter fireflies [61, 64]. It simply means that increasing the step length of the attraction may dominate the random movement or even take the solution out of the feasible region. A memory is utilized to save the best solution found and additional attraction term towards that global solution is added in [63, 69]. It is a good idea in which the best solution will not be lost through iteration. To increase the diversity of the solution, an effective modification is proposed in [110]. Using such kind of modification, the diversity of the solutions will be preserved, and the exploration behaviour of the algorithm will be improved.
\nBasically, two updating strategies are proposed for the non-continuous case. The first one is using the same updating formula and changing the results to discrete values afterwards [82, 94, 104]. And the second is to modify the updating formula on the discrete space [97–101]. The first problem is susceptible of trapping in local solution and misses the optimal solution. The optimal solution for a continuous version of a discrete problem may not always be an optimal solution for the discrete problem. Hence, the algorithm will tend to converge to the optimal solution of the continuous version of the problem. Hence, the second approach has an advantage in such cases.
\nThe comparison of results is performed between the standard firefly algorithm and non-parameter modified version, i.e. Class 2 and Class 3 modifications. The modified versions selected for simulation are based on two criteria, the first one clear modification, that is the modification should be clearly described, and the second one is with small number of new parameters. In some of the modifications, a number of new algorithm parameters are introduced and tuning this parameter by itself needs another study so they are not included in the simulation. The modified versions used for simulation include Firefly Algorithm 1 [32], FFA2, [52], FFA3 [53], FFA4 [26, 57], FFA5 [24, 59], FFA6 [58] FFA7 [60], FFA8 [61, 62], FFA9 [69], FFA10 [63] where
Five benchmark problems are selected from different categories as presented in Table 2. The simulations are performed on Intel® Core™i3-3110M CPU @ 2.40 Ghz 64 bit operating system. MATLAB 7.10.0 (R2010a) is used for these simulations. The algorithm parameters are set as given in Table 2 for dimensions 2 and 5.
\nThe simulation results, as presented in Table 3, show that some of the algorithms are very expensive in terms of computational time but give a good result, and others have small running time. For instance, in second problem, when the dimension is 2 on average, FFA3 outperforms all with average CPU time of 8.8, whereas FFA1 and FFA2 give a good approximation with smaller average CPU time. In general, it can be seen that FFA4 is very effective but not with the computational time. FFA1 and FFA2 give good approximate results with smaller CPU time compared to FFA4. However, when the dimension increases, FFA2 outperforms FFA1. Perhaps it is due to the fixed random direction m for all the simulations.
\n\n | Problems | \nRef. | \nProperties of the problem | \nParameters and set-up | \n|
---|---|---|---|---|---|
[111] | \nMultimodal Continuous Differentiable Non-separable | \n||||
[112] | \nMultimodal Continuous Non-differentiable Separable Non-scalable | \n||||
[113] | \nMultimodal Discontinuous Non-differentiable Separable | \n||||
[112] | \nUnimodal Continuous Differentiable Non-separable Non-Scalable | \n||||
[112] | \nUnimodal Continuous Non-differentiable Separable Scalable Stochastic | \n
Selected benchmark problems and simulation set-up.
\n | \n | F1 | \nF2 | \nF3 | \nF4 | \nF5 | \n|||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
\n | D | \n2 | \n5 | \n2 | \n5 | \n2 | \n5 | \n2 | \n5 | \n2 | \n5 | \n||||||||||
\n | \n | ||||||||||||||||||||
FFA | \n− 0.0195 | \n0.1002 | \n0.00 | \n0.00 | \n0.6696 | \n0.4483 | \n0.0504 | \n0.0135 | \n−6.6745 | \n1.2874 | \n−6.5854 | \n3.6570 | \n−193.75 | \n3.4097 | \n−166.46 | \n7.3072 | \n0.0158 | \n0.0148 | \n0.0477 | \n0.0373 | \n|
CPU | \n1.4 | \n0.3 | \n1.4 | \n0.0 | \n0.3 | \n0.1 | \n0.1 | \n0.0 | \n0.2 | \n0.1 | \n2.7 | \n0.1 | \n1.8 | \n0.4 | \n8.4 | \n2.5 | \n0.2 | \n0.0 | \n0.3 | \n0.1 | \n|
FFA1 | \n− 0.7185 | \n0.4220 | \n0.00 | \n0.00 | \n0.0039 | \n0.0085 | \n0.0109 | \n0.0052 | \n−7.6507 | \n0.00 | \n−17.135 | \n1.8541 | \n−199.61 | \n0.1757 | \n−195.44 | \n1.1286 | \n0.0005 | \n0.0006 | \n0.0002 | \n0.0003 | \n|
CPU | \n1.5 | \n0.3 | \n1.5 | \n0.1 | \n0.4 | \n0.1 | \n0.1 | \n0.0 | \n0.3 | \n0.1 | \n2.8 | \n0.2 | \n1.8 | \n0.4 | \n8.8 | \n2.4 | \n0.3 | \n0.0 | \n0.3 | \n0.0 | \n|
FFA2 | \n−0.7974 | \n0.4028 | \n0.00 | \n0.00 | \n0.0003 | \n0.0003 | \n0.0000 | \n0.0000 | \n−7.6507 | \n0.00 | \n−19.126 | \n0.0 | \n−199.98 | \n0.0136 | \n−199.98 | \n0.0076 | \n0.0047 | \n0.0092 | \n0.0048 | \n0.0102 | \n|
CPU | \n2.6 | \n0.4 | \n3.5 | \n0.3 | \n1.1 | \n0.1 | \n0.1 | \n0.0 | \n0.4 | \n0.1 | \n4.5 | \n0.4 | \n2.2 | \n0.5 | \n11.3 | \n2.9 | \n0.4 | \n0.1 | \n0.9 | \n0.2 | \n|
FFA3 | \n−0.0128 | \n0.0903 | \n0.00 | \n0.00 | \n0.4402 | \n0.3768 | \n0.0397 | \n0.0158 | \n−7.2621 | \n0.7406 | \n−8.6751 | \n2.9248 | \n−195.20 | \n2.6004 | \n−177.04 | \n6.5591 | \n0.0085 | \n0.0085 | \n0.0135 | \n0.0125 | \n|
CPU | \n1.3 | \n0.2 | \n1.4 | \n0.1 | \n0.3 | \n0.1 | \n0.1 | \n0.0 | \n0.2 | \n0.1 | \n2.6 | \n0.3 | \n1.6 | \n0.3 | \n8.1 | \n2.2 | \n0.2 | \n0.0 | \n0.2 | \n0.1 | \n|
FFA4 | \n−1.00 | \n0 | \n−1.00 | \n0.00 | \n0.0 | \n0.0 | \n0.00 | \n0 | \n−7.6507 | \n0.00 | \n−17.977 | \n1.9499 | \n−200 | \n0.0 | \n−200 | \n0.0 | \n0.0 | \n0.0 | \n0.00 | \n0.00 | \n|
CPU | \n44.2 | \n6.7 | \n60.4 | \n3.8 | \n8.8 | \n0.6 | \n1.6 | \n0.2 | \n4.2 | \n0.3 | \n126.5 | \n47 | \n52.56 | \n10.3 | \n574.8 | \n96.4 | \n4 | \n0.4 | \n5 | \n0.8 | \n|
FFA5 | \n−0.0372 | \n0.1711 | \n0.00 | \n0.00 | \n0.0078 | \n0.0169 | \n0.0113 | \n0.0038 | \n−7.6507 | \n0.00 | \n−17.610 | \n1.2629 | \n−199.58 | \n0.2164 | \n−194.51 | \n1.3993 | \n0.0013 | \n0.0013 | \n0.0009 | \n0.0007 | \n|
CPU | \n37.6 | \n5.8 | \n51.0 | \n2.4 | \n7.8 | \n0.8 | \n1.4 | \n0.1 | \n3.6 | \n0.3 | \n102.5 | \n43.1 | \n45.6 | \n9.3 | \n493.6 | \n80.1 | \n3.4 | \n0.3 | \n4.2 | \n0.8 | \n|
FFA6 | \n0.0000 | \n0.00 | \n0.00 | \n0.00 | \n0.7534 | \n0.5307 | \n0.0277 | \n0.0091 | \n−7.6507 | \n0.00 | \n−13.323 | \n0.8509 | \n−198.43 | \n0.7722 | \n−193.95 | \n1.4193 | \n0.0051 | \n0.0045 | \n0.0014 | \n0.0008 | \n|
CPU | \n1.4 | \n0.2 | \n1.5 | \n0.1 | \n0.3 | \n0.1 | \n0.1 | \n0.0 | \n0.3 | \n0.1 | \n2.7 | \n0.3 | \n1.6 | \n0.3 | \n8.6 | \n2.5 | \n0.2 | \n0.1 | \n0.3 | \n0.1 | \n|
FFA7 | \n0.0000 | \n0.00 | \n0.00 | \n0.00 | \n0.0664 | \n0.0330 | \n0.0007 | \n0.0002 | \n−7.6507 | \n0.00 | \n−12.286 | \n1.2151 | \n−199.98 | \n0.0136 | \n−196.05 | \n1.8438 | \n0.0001 | \n0.0002 | \n0.0000 | \n0.0001 | \n|
CPU | \n0.7 | \n0.1 | \n0.8 | \n0.1 | \n0.2 | \n0.1 | \n0.1 | \n0.0 | \n0.2 | \n0.1 | \n1.7 | \n0.2 | \n0.9 | \n0.2 | \n4.0 | \n1.3 | \n0.1 | \n0.0 | \n0.2 | \n0.0 | \n|
FFA8 | \n0.0000 | \n0.00 | \n0.00 | \n0.00 | \n0.7113 | \n0.5634 | \n0.0499 | \n0.0149 | \n−7.2207 | \n0.8647 | \n−6.2323 | \n3.2674 | \n−193.81 | \n3.7645 | \n−167.58 | \n6.8116 | \n0.0084 | \n0.0085 | \n0.0425 | \n0.0372 | \n|
CPU | \n3.6 | \n0.6 | \n6.8 | \n0.1 | \n1.3 | \n0.1 | \n0.1 | \n0.0 | \n0.9 | \n0.1 | \n7.6 | \n0.7 | \n4.9 | \n0.8 | \n29.4 | \n8.5 | \n0.8 | \n0.1 | \n1 | \n0.1 | \n|
FFA9 | \n−0.0303 | \n0.15 | \n0.00 | \n0.00 | \n1.1369 | \n0.6702 | \n0.1115 | \n0.0251 | \n−5.4108 | \n1.7004 | \n−1.0048 | \n13.039 | \n−188.39 | \n5.3655 | \n−104.37 | \n12.818 | \n0.0486 | \n0.0399 | \n0.0951 | \n0.0915 | \n|
CPU | \n0.3 | \n0.1 | \n0.2 | \n0.0 | \n0.2 | \n0.1 | \n0.0 | \n0.0 | \n0.2 | \n0.1 | \n0.5 | \n0.1 | \n0.4 | \n0.1 | \n0.7 | \n0.1 | \n0.1 | \n0.0 | \n0.1 | \n0.0 | \n|
FFA10 | \n0.0000 | \n0.00 | \n0.00 | \n0.00 | \n1.0466 | \n0.6202 | \n0.0403 | \n0.0203 | \n−6.7824 | \n1.0078 | \n−13.245 | \n1.6688 | \n−175.52 | \n12.883 | \n−172.68 | \n10.459 | \n0.0064 | \n0.0055 | \n0.0014 | \n0.0012 | \n|
CPU | \n3.5 | \n0.6 | \n11.1 | \n0.1 | \n1.2 | \n0.1 | \n0.1 | \n0.0 | \n0.9 | \n0.1 | \n9.3 | \n0.9 | \n4.7 | \n0.9 | \n64.2 | \n18.1 | \n1.0 | \n0.1 | \n1.4 | \n0.2 | \n|
FFA11 | \n0.0000 | \n0.00 | \n0.00 | \n0.00 | \n0.6357 | \n0.4193 | \n0.0514 | \n0.0129 | \n−6.7098 | \n1.0934 | \n−6.1737 | \n3.3915 | \n−194.49 | \n3.3451 | \n−165.94 | \n7.3183 | \n0.0208 | \n0.0186 | \n0.0471 | \n0.0324 | \n|
CPU | \n1.3 | \n0.3 | \n1.4 | \n0.1 | \n0.3 | \n0.1 | \n0.1 | \n0.0 | \n0.3 | \n0.1 | \n2.7 | \n0.3 | \n1.7 | \n0.3 | \n8.3 | \n2.3 | \n0.2 | \n0.1 | \n0.3 | \n0.1 | \n|
FFA12 | \n0.0000 | \n0.00 | \n0.00 | \n0.00 | \n0.6611 | \n0.5103 | \n0.0493 | \n0.0141 | \n−6.5986 | \n1.3430 | \n−6.8659 | \n4.0703 | \n−193.96 | \n3.4841 | \n−169.34 | \n8.7051 | \n0.0161 | \n0.0160 | \n0.0595 | \n0.0452 | \n|
CPU | \n1.5 | \n0.2 | \n1.5 | \n0.1 | \n0.4 | \n0.1 | \n0.1 | \n0.0 | \n0.3 | \n0.1 | \n2.9 | \n0.3 | \n1.8 | \n0.4 | \n8.4 | \n2.4 | \n0.3 | \n0.0 | \n0.3 | \n0.1 | \n|
FFA13 | \n0.0000 | \n0.00 | \n0.00 | \n0.00 | \n2.1137 | \n1.4840 | \n0.0977 | \n0.0315 | \n−3.3360 | \n3.2207 | \n−1.1917 | \n12.914 | \n−189.53 | \n4.8277 | \n−148.59 | \n12.7616 | \n0.1356 | \n0.1374 | \n1.4724 | \n2.4610 | \n|
CPU | \n5.4 | \n0.9 | \n16.1 | \n0.2 | \n2.4 | \n0.2 | \n0.3 | \n0.1 | \n1.7 | \n0.1 | \n28 | \n2.5 | \n7.7 | \n1.4 | \n122 | \n30.2 | \n1.7 | \n0.2 | \n1.8 | \n0.2 | \n|
FFA14 | \n−0.6313 | \n0.29 | \n0.2399 | \n0.4 | \n0.0960 | \n0.0829 | \n0.0107 | \n0.0037 | \n−2.3289 | \n7.0785 | \n−0.4888 | \n11.437 | \n−198.32 | \n0.8034 | \n−193.92 | \n1.4485 | \n0.0046 | \n0.0047 | \n0.0010 | \n0.0006 | \n|
CPU | \n1.5 | \n0.3 | \n1.5 | \n0.1 | \n0.4 | \n0.1 | \n0.1 | \n0.0 | \n0.3 | \n0.1 | \n2.7 | \n0.3 | \n1.8 | \n0.4 | \n8.5 | \n2.5 | \n0.2 | \n0.0 | \n0.3 | \n0.1 | \n
Simulation results.
In this chapter, a detailed review of modified versions of firefly algorithm is presented. The modifications are used to boost its performance for both continuous and non-continuous problems. Three classes of modifications are discussed for continuous problems. The first one being parameter level modification which will improve the performance of the algorithm. The second class is on the updating mechanism level, in which new updating equation or mechanisms are introduced. The last class is in the abstract level in which change of solution space and probability distribution of the randomness term are discussed. The strength and weakness of the approaches are also presented. Simulation results show that mutation-incorporated firefly algorithm gives better result with larger computational time, whereas versions of firefly algorithm with opposition-based learning and elitist movement for the brighter firefly give approximate solution with smaller computational time. Hence, if a proper way of implementation is used, mutation operator and elitist move of brighter firefly algorithm along with possible implementation of opposition-based approach may perform better.
\nVisual information from the eyes generates vast amounts of data for the human brain to process, and provides us with unparalleled clarity and insight into the world we live in. Imaging with terahertz (THz) radiation is a research field that has gained a lot of interest and is in the process of moving from research laboratories to commercial applications [1, 2, 3, 4]. As such, the THz research field has grown so much that it has become impossible for a single human to be able to keep track of all developments [4]. Nevertheless, it is possible to outline why there is great interest and potential in THz imaging technology. Most non-conductive materials and non-polar liquids are THz transparent, useful for non-invasive inspection of many multi-component or buried systems, such as paintings [5], electronic circuits [6], space shuttle panels [7] and carbon-fiber composites [8]. Other possibilities are the measurement of picosecond processes in semiconductors [9], quality control of pharmaceutical tablets [10] and non-invasive detection of explosive substances [11]. A plethora of fundamental material resonances, such as phonons, rotations of molecules and precessions of spins, are observable and controllable by THz radiation [12]. Bio-medical applications are highly alluring most notably because the THz photon energies are non-ionizing and high-water sensitivity gives rise to label-free diagnosis of diseases that alter water content, such as cancer [13] and diabetic foot syndrome [14]. There is also the possibility of damaging or repairing DNA with intense THz radiation [15].
With so many possible applications, the reason why THz radiation is barely used outside of laboratories is due to costs of current THz technology. In particular to imaging, the technology is either too expensive, too slow or sacrifices some detection capability (such as picosecond temporal resolution). This is because materials which are suitable for efficient THz detection simply do not exist. This has resulted in THz detector arrays normally working in either narrowbands [16] or needing cryogenic temperatures for sensitive detection [17]. However, microbolometer arrays have very large bandwidths at room temperature operation [18] and when combined with digital holography they can measure both the amplitude and phase of THz radiation [19, 20]. Unfortunately bolometers achieve frequency resolution with a frequency selective source and they do not offer picosecond temporal resolution. This is acceptable for some applications such as detecting concealed weapons, however for applications where time gated detection is used, for example in extracting depths of painting coatings in original art works [5], it becomes unfeasible. An another imaging technique is to project a THz image on an electro-optic crystal then use visible light CCD arrays to spatially map-out the THz field incident onto the crystal [21, 22]. This does not sacrifice the temporal resolution offered time-domain THz spectrometers, however this needs a regen-amplified Ti:Sapphire laser which makes the whole system big and expensive and has prevented the widespread adoption of this technology despite its capabilities. These imaging techniques are all far-field, apart from [21], meaning that they fail to see detail below
The aforementioned imaging approaches are the standard imaging techniques, relying on a detector array or raster scanning, however there is another alternative. Namely, using a spatially modulated light beam and a single-pixel detector to obtain an image [30]. Approaches based on this technique are commonly called
Single-pixel imaging theory concerns itself with obtaining an image of a scene using a detector that can only measure the total amplitude emanating from the scene. The simplest idea is to raster scan an aperture across the field-of-view, building the image pixel by pixel. However, as the aperture is made smaller and smaller, the signal reaching our detector is reduced. We could increase the light incident onto our detector and overcome detector-noise by simultaneously scanning more apertures during each measurement, an idea that originates with Yates in 1935 [32]. In Figure 1(a) we show the main principle of this idea; we have a light beam that is spatially modulated which propagates through an object and onto a detector with no spatial resolution. It is of the utmost importance that in each measurement we know which apertures were open and which were closed. Without this information we could never reconstruct an image of the object. Each measurement is the dot product of the spatial encoding mask and the transmission function of the object, which is mathematically expressed as
(a) Imaging with a single-element detector. An encoding mask spatially encodes a beam of radiation, then the beam passes through an object and onto the single-element detector. (b) Spatial encoding masks, where the first, second, third and fourth coloumns were constructed from Sylvester Hadamard, cyclic Hadamard, random and Fourier matrices respectively. The green triangle in the cyclic mask is there as a visual guide. (c) 2D image transform examples. Figure (a) was extracted from reference [
where
where the rows of matrix
The Sylvester-Hadamard matrices are binary, meaning that they are easily implemented, specifically with digital micromirror devices which are relatively cheap and have switch rates upto 20 kHz. The reconstruction technique can also be efficiently calculated by just doing the Fast Hadamard-Walsh transform, meaning one does not need to store
The Cyclic-Hadamard matrices, also known as Paley Type I and type II Hadamard matrices as they were first discovered by Paley in 1933 [36], are orthogonal and circulant matrices made of 1 s and -1 s. This means they have large noise robustness, easy binary implementation and they are constructed by having one vector,
Random masks constructed from Bernoulli matrices, or Gaussian random matrices, can also be made from 1 s and -1 s making for easy implementation using binary spatial light modulators. However, these matrices are not directly invertible and using a pseudo-inverse can create stability problems. Therefore convex minimization algorithms are usually used for image reconstruction [30, 31]. The main benefit of this masking approach is that is can be used for undersampling which can greatly reduce the total measurement time at the expense of complicated calculations. References [37, 38] were the first theoretical investigation and one can obtain their reconstruction scripts from reference [39], although reference [40] also freely provides their MATLAB scripts for another minimization algorithm called TVAL3 [41]. These algorithms can be slow, hence a mention needs to be given to reference [42] where Kowarlz et al. creates a pseudo-inverse matrix via Fourier-domain regularization that is able to recover images of quality similar to the slow minimazation algorithms, however with faster calculations based on matrix multiplication methods. Note, they also provide their MATLAB and Python scripts freely on github [43].
Fourier masks are those derived from the Fourier matrix. However, as this is just linear algebra representation of the Fourier transform and we are measuring real images (without imaginary numbers), then we do not need to measure the negative Fourier frequencies as they are just the complex conjugate of their positive frequency counterpart. The Fourier matrix is also orthogonal meaning it has noise robustness equal to the Hadamard matrices as well efficient image reconstruction algorithms, simply the Fast Fourier Transform. These masks, however, are not binary but require grayscale values which limits their deployability. Binary spatial modulators can accomplish this either by temporal dithering, at the expense of slower switch-rates, or by spatial dithering, which creates some quantization errors [34]. Nevertheless, these masks benefit from extensive literature based on the Fourier Transform and various image compression algorithms that can be reversed for image-undersampling procedures.
In this single-pixel imaging modality, the most crucial part is to create a spatially modulated beam. In this respect for the THz regime there are four main methods that can be employed; by creating a physical mechanical mask, by changing the electrical conductivity of a material via the injection/depletion of charge carriers, by controlling the refractive index of liquid crystal cells and by creating a spatially varied beam directly at the THz generation stage.
Creating a physical mask to modulate THz radiation has the great advantage that this is the easiest in terms of manufacturing with great modulation depth,
There is another modulation technique that falls in this mechanical category. Namely, mirror arrays where each mirror can be individually addressed. Such arrays already exist for the visible light regime in the form of digital micromirror arrays (DMD). However, DMD mirrors are with dimensions around 10
Schematic of a single pixel of the THz-SLM for normally incident terahertz waves. The pixel is composed of mirrors that are arranged in 4 rows and 8 columns. (a) OFF-state for a bias voltage of 0 V. all mirrors are inclined and incident terahertz radiation (red) is diffracted away (blue) from the transceiver. (b) ON-state for a bias voltage of 37 V. all mirrors are pulled down to the substrate and incident terahertz radiation (red) is reflected (blue) into the transceiver. (c) Schematic cross-sectional view of an unreleased mirror. The base of the mirror adheres to the parylene C, while the part to be released sits on the poly-Si. (d) Schematic cross-sectional view of a released mirror. The base of the mirror adheres to the parylene C, while the released part is inclined due to residual stress in the Cr-Cu-Cr mirror material. (e) Modulation contrast of the THz-SLM. The contrast exceeds a value of 0.5 for a working range from 0.97 THz to 2.28 THz with a maximum contrast of 0.87 at 1.38 THz. (f) Linear dependence of the detected modulated electric field on the number of switched-ON rows in the THz-SLM. (g) Linear dependence of the detected modulated electric field on the number of switched-ON columns in the THz-SLM. Figure reprinted from reference [
The main principle with this THz modulation technique is based upon the Drude model dielectric function [48, 49].
where
Optical based spatial THz-light modulators are currently the best in terms of achieved switch-rate, operational frequency and ease of implementation. Their switch-rate and operational frequencies are both similar to the electrical modulators in that they also rely on modifying the charge carrier density in some material. However, as they use optical light to achieve this, their experimental implementation is very different and due to the current state of visible-light SLMs they are much easier to be implemented. One starts by patterning a visible light beam and then projecting this spatial pattern onto a semiconductor, thereby creating areas that experience large optical excitation and other areas which are left in their ground state. This in turn creates a spatially varying conductivity/absorption profile on the surface of the semiconductor, and thus if a THz beam passes through this surface then the inverse spatial pattern from the visible-light beam is imparted onto the THz beam. The optical excitation can come in two forms, pulsed and continuous wave. For both cases, the carrier concentration is described by
for carrier generation rate
For continuous wave excitation one needs to consider the photo-carrier generation, recombination and diffusion dynamics within the semiconductor. The steady-state equilibrium carrier concentration within the semiconductor is given by [48].
where
(a) Carrier density (in arbitrary units) for different carrier lifetimes, shown by the colored numbers in ms, as we switch a continuous wave source on and off. (b) Illustration of imaging setup: Using a digital micromirror device and a lens, a pump pulse is spatially structured and projected onto a silicon wafer. This spatially modulates a coincident THz pulse. This THz pulse then passes through an object and is measured on a single-element THz detector. Inset is an optical image of a resolution test target (cartwheel) manufactured from gold on a 6
For pulsed optical-excitation probed by a synchronous THz pulse, Eq. (5) changes because the THz pulse can travel through the spatially photopumped region a few picoseconds after photoexcitation and for
Electrical based modulators are likely to be the long-term future solution for spatial THz modulators because they have very little fundamental limitations. Namely, the maximum switch-rates are limited by the carrier recombination rates meaning they can potentially achieve megahertz switch rates, provided the RC constants of the devices are taken into account, especially with electrically tunable materials such as graphene [58, 59]. Their size is determined by photolithographic manufacturing technologies, which is already orders of magnitudes smaller than the THz wavelengths meaning that pixel sizes can be highly subwavelength. In fact, sometimes THz modulation structures can be too large for some commercial photolithographic systems. They are fully self-contained and compact, which is their main advantage over the optical based modulators (see
One of the first demonstrations of this modulation technique was by Kleine-Ostmann et al. in 2004 [60] where they electronically depleted carriers from a GaAs/AlGaAs interface, achieving about 3% modulation across a broadband frequency range of 0.1 to 2 THz. Since then there have been numerous attempts at improving the modulation depth, see references [61, 62] for recent reviews. These efforts have included enhancing the interaction between the THz wave and the charge carrier regions by metamaterial structures [63, 64]. Others have recently used graphene as the modulator [65, 66]. Using metamaterials or Fabry-Perot type resonances to enhance the modulation depth has the trade-off of reducing the working frequencies of the modulator. A further note is that subwavelength grating structures can enhance the THz modulation over a broadband range [58] for the correct THz polarization.
In 2014 C. M. Watts et al. created an electrical based THz-SLM in reference [67], and Figure 4(a) shows their experimental schematic and part (b) shows an image of their SLM. They electrically change the THz absorption of a 2
(a) Schematic of the single-pixel imaging process utilizing an SLM. An image is spatially modulated by the metamaterial and the resulting radiation is sent to the single-pixel detector. (b) Photograph of the SLM (courtesy of K. burke, Boston College media technology services); total active area of the SLM is (4.8 mm
Another innovative approach to single-pixel imaging is to create a spatially patterned beam at the generation step, rather than generate a homogeneous beam that is then spatially modulated, which has the benefit of not needing a THz-SLM. For the terahertz regime, this can be accomplished by three possible ways. First, having an array of photoconductive antennas [70, 71, 72], however this approach suffers from antenna cross-talk and inefficiency problems arising from the small working-area of the antennas whilst occupying a large area. Further, such antennas arrays have only been used as detectors. The second method is to use an electro-optic (EO) crystal that converts visible-light to THz frequencies via non-linear polarization effects [73]. The generation of THz radiation is localized to where the visible light is, hence projecting a spatially varying light beam will generate a THz-beam with the same spatial features. This idea was implemented by references [74, 75] where they used a used a SLM to pattern an 800 nm femtosecond pulse and project that onto a ZnTe crystal. The third method is similar to the second one, with the difference being the use of a spintronic THz emitter instead of an electro-optic crystal. Here the inverse spin Hall effect is used to generate an ultrafast current transient that generates the THz radiation [76, 77, 78]. The spatial patterning is again done by a visible-light SLM since the THz generation is again localized to areas where the optical-pump was shined upon. This was demonstrated by Chen et al. in reference [79].
The similarities between the spintronic and electro-optic crystal approaches are that they both require femtosecond pulses with mJ/cm
Figure 5(a) shows the experimental setup of reference [79] which uses the spintronic emitter array approach. Note that the EO crystal approach is identical in that you only have to replace the spintronic emitter with the EO crystal and remove the magnetic field, then place the object as close as possible to the emitter array. One should note that the use of the second DMD in Figure 5(a) is only to correct the phase front induced by first DMD, which can also be achieved by the technique shown in the supplementary information of reference [82]. The spintronic emitter is nanometer thick hence Chen et al. was able to resolve metallic lines 6
(a) Schematic of the GHOSTEAM system. The spintronic THz emitter array (STEA) is excited by two-DMD-encoded fs laser pulses and generates spatially coded THz pulses. An object “CAEP” was placed in the near-field region (
Liquid crystal modulators work by the re-orientating the material molecules under an applied voltage. As the molecules are oblong, this changes the refractive index that an electro-magnetic wave experiences. Therefore these devices are great for phase modulation giving the greatest freedom in the values that the sampling matrix can take. In other words, they can theoretically project a Fourier matrix that has grayscale complex-values. Note that complex valued masks can be used in conjunction with an intensity only detector to obtain an image that has phase and amplitude information [83]. A liquid crystal based SLM for THz was computationally studied [84]. However, the re-orientation of the molecules is a slow process, and in the visible light regime liquid crystal displays are typically limited to below 100 Hz switch rates. Due to the longer THz wavelengths, thicker layers of liquid crystals are needed resulting in even slower switch rates. For this reason, liquid crystal spatial modulators for THz radiation have been limited mostly to applications where slow switching speeds are acceptable such as dynamically controllable lenses [85, 86], absorption [87] or polarization control [88].
The first demonstration of a single-pixel THz camera that uses a multi-pixel modulation approach3 was in 2008 by Chan et al. [89]. Therein the authors showed amplitude and phase imaging was possible. Since Chan showed single-pixel THz imaging with metallic masks [89], most publications up until now have focused on improving the implementation by showing proof-of-concept modulation/generation techniques as opposed to potential applications. Shortly after in 2009 spectroscopic imaging was demonstrated [90]. The next experiment was in 2012 by Shen et al. [46] where they used a spinning disc with random masks, but it should be noted that their experiment used an infrared and a THz source whilst using the same SLM. The first demonstration of an optical based SLM was by Shrekenhamer et al. in 2013 [49]. The same group then published an electrical based SLM for single-pixel THz imaging in 2014 [67]. The next developments showed in 2016 that such imaging systems can detect a sub-wavelength fissure (8
The potential applications and capabilities of single-pixel THz cameras are directly determined by the THz source and detector, rather than the technique used to impart a spatial pattern in a beam of THz radiation. As such, it is unlikely for there to be a single-solution for all practical applications. Therefore, it is valuable to discuss where each of the techniques in
The metallic/physical based masks, employed in
Modifying the Drude plasma frequency of a semiconductor via injection/depletion of charge carriers has great potential for compact integration of the entire imaging system, as long as electrical gating is used as it negates the need for an extra pump-laser. The drawback is that to ensure modulation depth over a large frequency range the Drude plasma frequency has to be sufficiently modified. For example, after photoexcitation of silicon if
Direct generation of a spatially varying THz beam,
Although the first experimental implementations of single-pixel cameras can be traced back to 1976 [97], such imaging approaches were not widely studied or implemented in the commercial world. The reason is that the serial measurement of such ideas can not compete with parallel data acquisition of imaging arrays. Further, compressed sensing techniques began gaining mainstream attention in 2006 after two publications [37, 38]. This coincides with the development of visible light spatial modulators thereby allowing the implementation of the ideas in references [37, 38]. Whilst inherently slower than imaging arrays, these single-pixel cameras are much more robust and easier to implement in areas where imaging array technology is unavailable. In particular, the terahertz frequency regime.
This book chapter began by outlining the current state of THz cameras. Then it discusses the background theory of single-pixel imaging techniques. Most of the chapter was dedicated to discussing the current state of spatial THz-light modulators for use in single-pixel THz imaging in
The most advanced THz-SLM at present are those based on optical excitation of semiconductors,
Ultimately, the development of single-pixel THz cameras is likely to proceed with optical modulators being used in university laboratories to optimize the algorithms and methodologies used in image recovery as well as synchronization of all the equipment. Simultaneously there will be an effort to develop electrical based array modulators that have fast-switch rates and large modulation depth over a broadband frequency range. Then the miniaturization of such modulator arrays will start and it is likely that at this point such commercially available THz-SLMs will become available from new specialized start-up companies. Spatially-generated THz beams will likely remain only in laboratories for fundamental studies of different systems, but are unlikely to be used for industrial and commercial applications mostly due to the requirement of pump powers of
This work was partially supported by the Research Grants Council of Hong Kong (project numbers 14206717 and 14201415), The Hong Kong Innovation and Technology Fund (project number ITS/371/16), The Engineering and Physical Sciences Research Council (grant number EP/S021442/1), and the Royal Society Wolfson Merit Award (EPM).
The authors declare no conflict of interest.
Atomic force microscope
Printed Circuit Board
Field-programmable gate array
Spatial light modulator
Terahertz
Electro-optic
Signa-to-noise ratio
Digital micromirror device
Vanadium Dioxide
Charge Coupled device
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world. The standard method for soil salinity assessment is based on a laboratory method that is cumbersome and gives rise to limitations for data-intensive works. The use of sensors for the assessment of the apparent electrical conductivity (EC) of soils offers a way to overcome these constraints. These sensors are based on three electromagnetic phenomena, namely, electrical resistivity, electromagnetic induction, and reflectometry. Each class of sensors presents its own advantages and drawbacks. 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Over the past few years, the full access has been the objective of intense research efforts. Progress in this area has played an important role in opening up the possibility of using THz electromagnetic radiation (T-waves) in science and in real-world applications. T-waves are not perceptible by the human eye, are not ionizing, and have the ability to cross many non-conducting materials such as paper, fabrics, wood, plastic, and organic tissues. Moreover, the use of THz radiation allows non-destructive analysis of the materials under investigation both by study of their “fingerprint” via spectroscopic measurements and by high-resolution spatial imaging operations, exploiting the see-through capability of T-waves. Such technology can be applied in diverse areas, spanning from biology to chemical, pharmaceutical, environmental sciences, etc. In this chapter, we will present the typical architecture of measurement systems based on the THz technology, detailing what are the parameters that define their performance, the measurement methods, and the related errors and uncertainty, and focusing at the end on the use of time-domain spectroscopy for the evaluation of different material properties in this specific frequency region.",book:{id:"4622",slug:"new-trends-and-developments-in-metrology",title:"New Trends and Developments in Metrology",fullTitle:"New Trends and Developments in Metrology"},signatures:"Leopoldo Angrisani, Giovanni Cavallo, Annalisa Liccardo, Gian\nPaolo Papari and Antonello Andreone",authors:[{id:"2330",title:"Dr.",name:"Leopoldo",middleName:null,surname:"Angrisani",slug:"leopoldo-angrisani",fullName:"Leopoldo Angrisani"},{id:"179111",title:"Prof.",name:"Antonello",middleName:null,surname:"Andreone",slug:"antonello-andreone",fullName:"Antonello Andreone"},{id:"186826",title:"MSc.",name:"Giovanni",middleName:null,surname:"Cavallo",slug:"giovanni-cavallo",fullName:"Giovanni Cavallo"},{id:"186827",title:"Dr.",name:"GianPaolo",middleName:null,surname:"Papari",slug:"gianpaolo-papari",fullName:"GianPaolo Papari"},{id:"186828",title:"Prof.",name:"Annalisa",middleName:null,surname:"Liccardo",slug:"annalisa-liccardo",fullName:"Annalisa Liccardo"}]},{id:"49823",doi:"10.5772/60442",title:"Microwave Power Measurements: Standards and Transfer Techniques",slug:"microwave-power-measurements-standards-and-transfer-techniques",totalDownloads:2171,totalCrossrefCites:5,totalDimensionsCites:5,abstract:"In this chapter, precision power measurement, which is probably the most important area in RF and microwave metrology, will be discussed. Firstly, the background of RF and microwave power measurements and standards will be introduced. Secondly, the working principle of primary power standard (i.e., microcalorimeter) will be described, followed by the discussions of direct comparison transfer technique. 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The purpose of nonacoustic signals is to allow silent communication. One of these methods based on the electromyography signal is generated by the human speech articulation system. This article presents a device for electromyographic (EMG) signal acquisition and the first measurements from its use.",book:{id:"4622",slug:"new-trends-and-developments-in-metrology",title:"New Trends and Developments in Metrology",fullTitle:"New Trends and Developments in Metrology"},signatures:"Andrzej B. 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Important effects like the pre-distortion using adaptive techniques, with distinct characteristics like amplitude, phase, and frequency, as well as, their specific nature such as AM/AM, AM/PM, PM/AM, and PM/PM, and constitute one of the main directions of this research. All theoretical and technological approaches have been supported by a consistent set of numerical data performed with one of the most important platform of simulations used in the great area of Radio Frequency (RF) and Microwave structures. As a direct application, we are introducing some efficient processes that can be used for the characterization of RF systems with a set of consistent laboratorial measures that permit us to visualize the effective cost and a complete architecture for the characterization of high-power amplifiers. With the continuous and innovative technological demand that is imposed by the international marketing has a great importance to find versatile systems that are capable of measuring several amplifier characteristics, as gain, output power, inter-modulation distortion of different signals, efficiency, current, and temperature that constitute another direction of research that has been demanded strongly for news advanced technologies used widely in modern HDTV systems.",book:{id:"4622",slug:"new-trends-and-developments-in-metrology",title:"New Trends and Developments in Metrology",fullTitle:"New Trends and Developments in Metrology"},signatures:"Daniel Discini Silveira, Marcos Paulo de Souza Silva, Marcel Veloso Campos and Maurício Silveira",authors:[{id:"179507",title:"Dr.",name:"Mauricio",middleName:null,surname:"Silveira",slug:"mauricio-silveira",fullName:"Mauricio Silveira"},{id:"179508",title:"Dr.",name:"Daniel",middleName:null,surname:"Discini Silveira",slug:"daniel-discini-silveira",fullName:"Daniel Discini Silveira"},{id:"179792",title:"Dr.",name:"Marcos Paulo De Souza",middleName:null,surname:"Silva",slug:"marcos-paulo-de-souza-silva",fullName:"Marcos Paulo De Souza Silva"},{id:"180084",title:"Dr.",name:"Marcel Veloso",middleName:null,surname:"Campos",slug:"marcel-veloso-campos",fullName:"Marcel Veloso Campos"}]}],mostDownloadedChaptersLast30Days:[{id:"49823",title:"Microwave Power Measurements: Standards and Transfer Techniques",slug:"microwave-power-measurements-standards-and-transfer-techniques",totalDownloads:2170,totalCrossrefCites:5,totalDimensionsCites:5,abstract:"In this chapter, precision power measurement, which is probably the most important area in RF and microwave metrology, will be discussed. Firstly, the background of RF and microwave power measurements and standards will be introduced. Secondly, the working principle of primary power standard (i.e., microcalorimeter) will be described, followed by the discussions of direct comparison transfer technique. Finally, there will be some discussions about the performance evaluation and uncertainty estimation for microwave power measurements.",book:{id:"4622",slug:"new-trends-and-developments-in-metrology",title:"New Trends and Developments in Metrology",fullTitle:"New Trends and Developments in Metrology"},signatures:"Xiaohai Cui, Yu Song Meng, Yueyan Shan and Yong Li",authors:[{id:"100680",title:"Dr.",name:"Yueyan",middleName:null,surname:"Shan",slug:"yueyan-shan",fullName:"Yueyan Shan"},{id:"135408",title:"Dr.",name:"Xiaohai",middleName:null,surname:"Cui",slug:"xiaohai-cui",fullName:"Xiaohai Cui"},{id:"173971",title:"Dr.",name:"Yu Song",middleName:null,surname:"Meng",slug:"yu-song-meng",fullName:"Yu Song Meng"}]},{id:"50396",title:"Electrical Conductivity Measurements in Agriculture: The Assessment of Soil Salinity",slug:"electrical-conductivity-measurements-in-agriculture-the-assessment-of-soil-salinity",totalDownloads:4414,totalCrossrefCites:11,totalDimensionsCites:24,abstract:"Soil salinity is an important issue constraining the productivity of irrigation agriculture around the world. The standard method for soil salinity assessment is based on a laboratory method that is cumbersome and gives rise to limitations for data-intensive works. The use of sensors for the assessment of the apparent electrical conductivity (EC) of soils offers a way to overcome these constraints. These sensors are based on three electromagnetic phenomena, namely, electrical resistivity, electromagnetic induction, and reflectometry. Each class of sensors presents its own advantages and drawbacks. In the following chapter, these are presented along with the most popular commercial EC sensors used in nowadays agriculture, equations for the assessment of soil salinity on basis sensor measurements, some examples of application, and present and future development trends.",book:{id:"4622",slug:"new-trends-and-developments-in-metrology",title:"New Trends and Developments in Metrology",fullTitle:"New Trends and Developments in Metrology"},signatures:"Fernando Visconti and José Miguel de Paz",authors:[{id:"79081",title:"Dr.",name:"Fernando",middleName:null,surname:"Visconti",slug:"fernando-visconti",fullName:"Fernando Visconti"}]},{id:"51435",title:"Objectifying the Subjective: Fundaments and Applications of Soft Metrology",slug:"objectifying-the-subjective-fundaments-and-applications-of-soft-metrology",totalDownloads:1735,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"The aim of the interdisciplinary research was to facilitate the understanding of a specific topic passing by different disciplinary perspectives. Soft metrology is the perfect example of a scientific field that needs that sort of approach. Seeking to provide a reproducible basis for qualifying and quantifying what are essentially ‘soft’ measurements (subject to human perception and interpretation) is a particularly challenging scientific endeavour. This chapter presents a theoretical overview of main concepts around soft metrology and, in the second instance, proposes a mathematical model for the measurement of a soft measurand through a dedicated index (IPER—influence on performance index).",book:{id:"4622",slug:"new-trends-and-developments-in-metrology",title:"New Trends and Developments in Metrology",fullTitle:"New Trends and Developments in Metrology"},signatures:"Laura Rossi",authors:[{id:"187245",title:"Dr.",name:"Laura",middleName:null,surname:"Rossi",slug:"laura-rossi",fullName:"Laura Rossi"}]},{id:"50379",title:"Uncertainty of Measurement in Medical Laboratories",slug:"uncertainty-of-measurement-in-medical-laboratories",totalDownloads:3829,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"The “Guide to the Expression of Uncertainty in Measurement” (GUM) is not systematically used in medical laboratories, for what the laboratorian should understand the Uncertainty Approach and its importance to recognize the level of realism of results. This chapter presents, discusses, and recommends the models fulfilling GUM principles. An example is given to a single test for an easier understanding of the determination of measurement uncertainty. All the practice uses a freeware. Results with larger measurement uncertainty intervals have a significant probability of being unrealistic, arising a high risk of the uncorrected clinical decision. A flow chart to the selection of models for the determination of measurement uncertainty in a medical laboratory is recommended.",book:{id:"4622",slug:"new-trends-and-developments-in-metrology",title:"New Trends and Developments in Metrology",fullTitle:"New Trends and Developments in Metrology"},signatures:"Paulo Pereira",authors:[{id:"178637",title:"Dr.",name:"Paulo",middleName:null,surname:"Pereira",slug:"paulo-pereira",fullName:"Paulo Pereira"}]},{id:"51241",title:"Innovative Theoretical Approaches Used for RF Power Amplifiers in Modern HDTV Systems",slug:"innovative-theoretical-approaches-used-for-rf-power-amplifiers-in-modern-hdtv-systems",totalDownloads:1348,totalCrossrefCites:4,totalDimensionsCites:4,abstract:"The essential purpose of this chapter is to introduce theoretical and numerical approaches that can be used for modeling nonlinear effects that appear intrinsically in the design of power amplifiers that have been used widely in many modern high-density television (HDTV) architectures. Important effects like the pre-distortion using adaptive techniques, with distinct characteristics like amplitude, phase, and frequency, as well as, their specific nature such as AM/AM, AM/PM, PM/AM, and PM/PM, and constitute one of the main directions of this research. All theoretical and technological approaches have been supported by a consistent set of numerical data performed with one of the most important platform of simulations used in the great area of Radio Frequency (RF) and Microwave structures. As a direct application, we are introducing some efficient processes that can be used for the characterization of RF systems with a set of consistent laboratorial measures that permit us to visualize the effective cost and a complete architecture for the characterization of high-power amplifiers. With the continuous and innovative technological demand that is imposed by the international marketing has a great importance to find versatile systems that are capable of measuring several amplifier characteristics, as gain, output power, inter-modulation distortion of different signals, efficiency, current, and temperature that constitute another direction of research that has been demanded strongly for news advanced technologies used widely in modern HDTV systems.",book:{id:"4622",slug:"new-trends-and-developments-in-metrology",title:"New Trends and Developments in Metrology",fullTitle:"New Trends and Developments in Metrology"},signatures:"Daniel Discini Silveira, Marcos Paulo de Souza Silva, Marcel Veloso Campos and Maurício Silveira",authors:[{id:"179507",title:"Dr.",name:"Mauricio",middleName:null,surname:"Silveira",slug:"mauricio-silveira",fullName:"Mauricio Silveira"},{id:"179508",title:"Dr.",name:"Daniel",middleName:null,surname:"Discini Silveira",slug:"daniel-discini-silveira",fullName:"Daniel Discini Silveira"},{id:"179792",title:"Dr.",name:"Marcos Paulo De Souza",middleName:null,surname:"Silva",slug:"marcos-paulo-de-souza-silva",fullName:"Marcos Paulo De Souza Silva"},{id:"180084",title:"Dr.",name:"Marcel Veloso",middleName:null,surname:"Campos",slug:"marcel-veloso-campos",fullName:"Marcel Veloso Campos"}]}],onlineFirstChaptersFilter:{topicId:"749",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"August 12th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. 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His later study in cooperation with experts in nephrology and immunology resulted in the designation of the new diagnostic method of UTI, patented in 2017. He is currently working at the Department of Microbiology, Medical University of Gdańsk (GUMed), Poland. Since many years, he is a member of steering committee of Gdańsk branch of Polish Society of Microbiologists, a member of ESCMID. 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Her research interest is in antibiotic resistance, host-pathogen interaction, and therapeutics development for staphylococcal pathogens, mainly Staphylococcus aureus, which causes hospital-acquired infections. Currently, her research is mostly focused on the study of oral pathogens, particularly Staphylococcus spp.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorThree:null},{id:"4",title:"Fungal Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",isOpenForSubmission:!0,editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. 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He is currently a rated researcher by the National Research Foundation of South Africa at category C2. He has published widely in the field of infectious diseases and has overseen several MSc’s and PhDs. His research activities mostly cover topics on infectious diseases from epidemiology to control. His particular interest lies in the study of intestinal protozoan parasites and opportunistic infections among HIV patients as well as the potential impact of childhood diarrhoea on growth and child development. He also conducts research on water-borne diseases and water quality and is involved in the evaluation of point-of-use water treatment technologies using silver and copper nanoparticles in collaboration with the University of Virginia, USA. 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He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. 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Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}]},{type:"book",id:"7123",title:"Current Topics in Neglected Tropical Diseases",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7123.jpg",slug:"current-topics-in-neglected-tropical-diseases",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Alfonso J. 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He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. 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He obtained a Master’s degree in Public Health and PhD in Public Health and Epidemiology. He has a background in Clinical Medicine and has taken courses at higher diploma levels in public health from University of Transkei, Republic of South Africa, and African Medical and Research Foundation (AMREF) in Nairobi, Kenya. Dr. Kasenga worked in different places in and outside Malawi, and has held various positions, such as Licensed Medical Officer, HIV/AIDS Programme Officer, HIV/AIDS resource person in the International Department of Diakonhjemet College, Oslo, Norway. He also managed an Integrated HIV/AIDS Prevention programme for over 5 years. He is currently working as a Director for the Health Ministries Department of Malawi Union of the Seventh Day Adventist Church. Dr. Kasenga has published over 5 articles on HIV/AIDS issues focusing on Prevention of Mother to Child Transmission of HIV (PMTCT), including a book chapter on HIV testing counseling (currently in press). 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This phenomenon has been studied by scientists for many years. However, clear strategies and actions are needed now more than ever. Every day, humanity, from individuals to businesses (public and private) and governments, are called to change their mindset in order to pursue a virtuous combination for sustainable development. Reasoning in a sustainable way entails, first and foremost, managing the available resources efficiently and strategically, whether they are natural, financial, human or relational. In this way, value is generated by contributing to the growth, improvement and socio-economic development of the communities and of all the players that make up its value chain. In the coming decades, we will need to be able to transition from a society in which economic well-being and health are measured by the growth of production and material consumption, to a society in which we live better while consuming less. In this context, digitization has the potential to disrupt processes, with significant implications for the environment and sustainable development. There are numerous challenges associated with sustainability and digitization, the need to consider new business models capable of extracting value, data ownership and sharing and integration, as well as collaboration across the entire supply chain of a product. In order to generate value, effectively developing a complex system based on sustainability principles is a challenge that requires a deep commitment to both technological factors, such as data and platforms, and human dimensions, such as trust and collaboration. Regular study, research and implementation must be part of the road to sustainable solutions. Consequently, this topic will analyze growth models and techniques aimed at achieving intergenerational equity in terms of economic, social and environmental well-being. 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