Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\n
This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\n
We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\n
Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n
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1. Introduction
Stem borers constitute the most widely distributed and injurious group of insect pests of cereal crops. They are commonly known to be one of the limiting factors of cereal production worldwide. They are present in field throughout the crop growing stage from seedling to maturity. The stem borers found on cereal crops in Africa are mainly lepidopterans and dipterans. Cereal crops such as rice, sorghum, maize, sugarcane and pearl millet suffer from the attack of stem borers. The larval stage constitutes the most damaging developmental stage of the pest. They are concealed inside the stem where they feed on the internal cavity of the plant making them very difficult to control. Stem borers cause severe damage on plant stems particularly the destruction of the central leaves (dead-heart) and the drying of the panicle (white head). Their attack leads to significant yield losses. According to [1], the most serious pests of cereal crops in Africa include stem borers. The severity of damage depends not only on the species and density of the pest but also on the phenology stage of the crops. Yield losses of about 10 and 100% due to stem borer have been recorded in rice fields [2].
To control these pests, various strategies have been practiced. These include cultural practices, host plant resistance, habitat management, biocontrol and the use of synthetic pesticides. Each management method has some advantages and limitation regarding its impact on environment, human health and its economic costs and sustainability. Nowadays, the integrated management, combining two or several of these management methods appears to be the most effective and sustainable option. This chapter comprises two main sections. The first section gives an overview of the main stem borers and their host range in Africa and the second section describes the various management options used to control stem borers and discusses the advantage and limitation of each method while exploring option of combining multiple methods to sustainably mitigate the effect of the stem borer on stakeholder farmers in Africa. This chapter will support the current research on the sustainable management of stem borers attacking cereal crops and will contribute to increase their productivity in Africa.
2. Overview of the main stem borers of cereal crops in Africa
Several stem borers species have been reported to cause severe damage on various cereals crop in Africa.
The maize stalkborer, Busseola fusca (Lepidoptera: Noctuidae) is reported to be of economic importance for maize and sorghum [3] while maintaining its population on some alternative hosts. The spotted stem borer, Chilo partellus (Lepidoptera: Pyralidae) is considered to be one of the devastating stem borer of sorghum and maize [4, 5] and also makes severe damage on rice in some African countries (Togola, unpublished data). The African striped rice borer, Chilo zacconius Bleszynski (Lepidoptera: Pyralidae) is among the major rice stem borer species occurring in the humid forest and savanna zones according to Akinsola [6]. The host range comprises cultivated rice, wild rice, Oryza longistaminata, Panicum sp., and Paspalum scrobiculatum but it has been found also on maize [7]. Other host plants include Pennisetum spp., Rottboellia cochinchinensis (Loureiro) W.D. Clayton, Saccharum officinarum L and Sorghum arundinaceum (Desv.) Stapf. [6, 8, 9]. The pink Stem Borer Sesamia calamistis (Noctuidae) is generally less important than Busseola fusca and Chilo partellus as a pest of cereal crops in Africa but may be locally abundant. It attacks sorghum, maize, rice and sugarcane as main host. It can be found also on wheat and pearl millet.as secondary crop host and at less extend on wild grass such as Pennisetum purpureum, Setaria sp., Rottboellia exaltata and Cyperus distans as alternative host [10, 11]. The millet stem borer Coniesta ignefusalis Hampson (Lepidoptera: Pyralidae) is an important pest of pearl millet (Pennisetum glaucum (L.) especially in West Africa [12]. The damage caused by C. ignefusalis is estimated to 15 and 100% of crop losses annually, depending on location and season [13]. The last generation enters diapause at the beginning of the dry season and stay for 6 months the time the next growing season comes. The sugarcane stem borer Eldana saccharina (Pyralidae) is a stem borer of cereal crops in Africa with particular economically importance on sugarcane. In the past E. saccharina appeared to be of very little important pest in Africa, except on sugarcane. But it has recently increased in importance on other crops such as maize, and sorghum in several African countries [14]. Also it can attack rice. Its hosts among wild grasses are Panicum maximum, Cyperus papyrus [15], Sorghum halepense, S. verticilliflorum and Pennisetum purpureum. The white rice borer Maliarpha separatella Ragonot (Lepidoptera: Pyralidae) is an important stem-borer of rice in West Africa [16]. The larva bores into the stem from the lowest internode where it feeds on the internal tissue preventing the nutrient to rise up until the panicle. The damage caused by this stem borer is said to be unique among rice stem borers because it rarely causes deadhearts or whiteheads [7]. The symptoms of M. separatella damage are similar to that of the sheath rot caused by a fungus pathogen Sarocladium oryzae. The stem of the infested plant becomes weakened, the panicles incompletely exerted from the flag leaf and the grains incompletely filled with brown coloration. M. separatella is more severe in low land, irrigated and floated rice. It also attacks the wild rices Oryza longistaminata and Oryza punctata [17]. It was also reported on some wild grasses such as Andropogon tectorum and Echinochloa holubii [18]. The rice yellow stem borer Scirpophaga spp (Lepidoptera: Pyralidae) are among the minor rice stem borers in Africa. Several species of Scirpophaga exist but the most dominant in Africa is S. melanoclista Meyrick [7]. The stalk-eyed fly Diopsis spp. (Diptera: Diopsidae) is a serious pest of rice in Africa. The two main species commonly found in rice in Africa are Diopsis thoracica Westwood and Diopsis apicalis Westwood [7]. Diopsid can be found in all rice ecological zones but preferentially in humid and shady lowland [19, 20, 21] and also in irrigated rice fields [7]. Damage from Diopsid larvae is similar to the primary damage made by Lepidopteran larvae resulting to the death of the central leaf of rice plant (deadheart). Feeding by the larvae significantly reduces the tiller density, the effective panicles, the grains weight and the total yield [7] and increases the number of immature panicles. The damage level increases according to Diopsis density. In endemic area 60% of the tillers can be infested [22]. Finally the African rice gall midge (AfRGM), Orseolia oryzivora Harris and Gagné (Diptera: Cecidomyiidae) is an indigenous dipteran borer of rice that was first reported from southern Sudan in 1947 [23, 24]. The pest is now spread in more than 20 African countries where severe yield losses have been reported. The damage converted the shoot meristem into a gall. The infested plant is no longer able to develop into a floral meristem and then the reproductive potential of the plant is severely compromised [23]. Larval feeding causes severe damage to rice during the vegetative stages (seedling to panicle initiation). Heavy yield losses of 45–80% in farmers’ rice crops have been recorded in some fields [25, 26].
A clear knowledge of these stem borer species and their host crop are of key importance for a sustainable management action.
3. Management of the main stem borers of cereal crops in Africa
Because of the nature of the habitat of stem borers (internal shelter), their management requires some specific control measures and actions. Various strategies exist for managing stem borers’ population and damage in cereals crops. These include cultural practices, host plant resistance, biocontrol and use of synthetic pesticides.
3.1 Preventive cultural practices
Cultural practices are considered as classic pest control methods. This method consist of manipulating the cropping systems in order (1) to avoid the meeting of crop susceptible stage with pest highest density or (2) to improve the crop growing condition or (3) to make the environment unfavorable for pest proliferation. The cultural practices have the advantage to be easy to implement with less cost. They are more convenient for smallholder farmers in developing countries [27]. Preventives cultural practices comprise a wide range of agronomic practices. These tactics need to be undertaken as first line defense measures to prevent high infestation of stem borers in cereals fields. Among the most effectives cultural practices in controlling stem borers there is cereals intercropping or strip cropping with non-host crops such as cowpea, soybeans and groundnut. Also the choice of appropriate date for planting cereals crops allows the crops to escape to critical period where the pest pressure is high [28]. The sol fertilization and field hygiene are cultural practices that reinforce the plant vigor and increase its defense system. [29, 30] demonstrated that zinc fertilization and potassium fertilization significantly decrease stem borers population in rice and increase paddy yield. Other practice such as destruction of crop residues (burning, plowing or disking) appears to be an effective cultural tactic for limiting the number of diapausing larva of stem borer. [31] demonstrated that plowing and disking crop residues destroyed 24% of the stem borers’ population on sorghum and 19% of maize stem borers. Similarly, [32] reported that the destruction of sugarcane residue after harvest significantly reduced the infestation of subsequent crops by Eldana saccharina. Burning of crop residues was also reported to be effective against Chilo spp. and Busseola fusca as well [15]. Burning or composting old stalks before the onset of the rains is effective against B. fusca [13]. The management of the maize stalkborer B. fusca includes intercropping maize with non-hosts crops like cassava and cowpea or with a repellent plant such as silver leaf desmodium (Desmodium uncinatum) [33]. Others cultural practices such as destruction of alternatives host plants or ratoons, synchronized plantings, crop rotations, high cropping density, use of trap crops, good irrigation and good fertilization are good cultural tactics against the insect pests in general and stem borers in particular [27]. The use of trap crops or intercropping upland NERICA rice and maize have also been suggested as an effective method for controlling M. separatella in rice ecosystems in Nigeria [26]. Practices such as irrigation, planting density and dates of planting were all found to be effective as well important factors for consideration [17, 34]. According to [24], the management of the African rice gall midge take into account early and synchronized planting as rice fields planted early are less likely suffer serious damage than those planted late. Also destruction of alternative host plants such as rice ratoons, volunteers and Oryza longistaminata as well as the use of moderate levels of fertilizer (e.g. 60 kg/ha) prevent the build-up of AfRGM population. The same author highlighted the importance of plant spacing as close spacing provides a suitable micro-environment for the survival of the exposed life stages of AfRGM. Cleaning of the rice field especially the destruction of the wild rice are good cultural practice for managing the African striped rice borer, C. zacconius. The ‘push-pull’ method based on the intercropping of Desmodium with millet was report to effectively act as a repellent that ‘pushes’ the millet stem borer C. ignefusalis away from the millet [35]. The most useful advantage of the cultural practices is that they are compatible to all pest control measures. They represent an important component of the integrated pest management of the stems borers. The main disadvantage of the cultural practices is that they need to be continuous and collective process from field preparation to harvest. A good cultural practices field can get infested if the surrounding farmers do not apply same or no management option.
3.2 Varietal resistance
Plant resistance is the genetically inherited qualities that confer the plant ability to ward off or withstand pest attacks or recover from injury due to a pest [36, 37]. This method is the most farmer-friendly pest control option that can significantly reduce stem borer damage when supplemented with other options such as cultural or biological measures. It is most attractive as the use of insecticides is largely beyond the means of the small farmer. Considerable progress has been made in screening and breeding for host plant resistance to cereals’ stem borers but only limited number of varieties have shown good level of resistance. Wiseman [38] showed that the resistant cultivar should be the base from which integrated pest management strategies arise. Rana et al. [39] reported that antibiotic property in sorghum plays more role in plant resistance to stem borer than ovipositional non-preference. Some plant biophysical characters such as stem hardness, leaf hairiness are important in plant resistance to stem borers. Sorghum varieties having these traits are rejected by the moths for oviposition. Pearl millet varieties such as Zongo was reported to be moderately resistant to Coniesta ignefusalis [40]. According to [41], hairiness of leaves and leaf sheaths were partly responsible for the differences in genotypic vulnerability to C. ignefusalis. Also they reported that plants with trichomes were not preferred by this pest for oviposition. [42] reported good level of resistance in the sweet sorghums BR 501, BR 504, and BR 505 to the sugarcane borer Eldana saccharina. [20], found good source of resistance to diopsids among upland NERCA varieties. Also, they reported that rice varieties having ability to produce new tillers to compensate the infested stems can tolerate the damage by diopsid. So far no improved rice variety was identified to be resistant to O. oryzivora attack but some tolerance was noted in Oryza glaberrima and also in some improved released rice varieties in Nigeria such as Cisadane and FARO 51 [24]. Despite limited achievement on varietal resistance to stem borer, this option remain a promising IPM component. Recent advances in biotechnology can increase the prospects of generating resistance materials and accelerate the transfer of gene for improving new genotypes.
3.3 Biological control measures
Biological control is the manipulation of natural enemies with the aim to maintain pest population below the economic injury level (EIL). Several organisms such as insects, fungus, virus and bacteria can be used as biocontrol agents [13, 43]. Insects based organisms acting as natural enemies are either predators (using the host as food) or parasitoids (laying their eggs in the host). Most of these insects belong to hymenoptera or diptera orders [27]. Biocontrol appears to be one of the most effective and environment friendly management option of stem borers. Indeed, stem borer’s population and damage can be regulated by sustaining the action of natural enemies. This can be done through a good habitat management to favor the buildup of the population of natural enemies (spiders, wasps, ladybirds, etc.) or through mass rearing and field release of specific parasitoids to control target pest species. The success of the release of several parasitoids was reported in managing cereals stem borers in Africa. Two natural enemies of the maize stalkborer (B. fusca) are the larval parasitoids Cotesia sesamiae and Bracon sesamiae [31, 44]. Parasitoids such as Tetrastichus atriclavus, Apanteles sesamiae, and Pediobius furvus have been reported by [13] to be most important parasites of B. fusca. Similarly Cotesia flavipes and Xanthopimpla stemmator was reported to effectively control the spotted stem borer C. partellus [4]. The parasitoids Cotesia sesamiae, Xanthopimpla stemmator, Trichogramma spp, etc. are cited as good biocontrol agent against the pink Stem Borer Sesamia calamistis (Togola, unpublished data). The biological control of the cereal stem borers is mainly based on habitat management to sustain natural enemies including various parasitoids wasp. [45] found that that the contribution of egg parasitism is more important in controlling lepidopteran stem borers than parasitism of larvae and pupae. The African rice gall midge (AfRGM) is attacked by two parasitoids such as Platygaster diplosisae (Hymenoptera: Platygastridae) and Aprostocetus procerae (Hymenoptera: Eulophidae) that can decrease the population of the pest below the economic injury threshold in rice-production systems [25]. Several insects species such as Cyrtorhinus viridis (Heteroptera: Miridae), Conocephalus longipennis (Orthoptera: Tettigoniidae) and Anaxipha longipennis (Orthoptera: Gryllidae) are predators of AfRGM [24]. The effectiveness and sustainability of the biological control methods depends on the availability of the biocontrol agent at suitable density. Practices such as habitat management or avoiding the use of wide spectrum chemicals can contribute to increase the carry-over of population of natural enemies and maintain the pest population below a critical level. The main constraints of the biocontrol measures are the difficulty to find the specific biocontrol agents for targeted pest species, the complexity of the mass rearing and the complication to be explained by extension workers and to be implemented by farmers.
3.4 Chemical control
Chemical control, despite all the danger and environmental hazard, remains an important option to consider in situation where the pest population is already established. Also it can be used as IPM component to supplement varietal resistance or cultural practices. Chemical control can be achieved by applications of granules or dusts to the leaf whorl early in crop growth to kill early larval instars of E. saccharina [10]. Controlling M separatella using chemical insecticides is effective but not widely practiced because of the high costs involved [46]. As for O. oryzivora, chemical control can be envisaged in conditions of high infestation of rice field. In all cases, choice of selective systemic insecticides is needed to avoid adverse effects on non-target organisms and biodiversity.
3.5 Integrated pest management option
The individual control methods discussed above have their limitations and none often is sufficient to adequately control stem borer outbreaks. Hence, the integrated pest management (IPM), also known as integrated pest control (IPC) appears to be the most appropriate option for managing these pests. IPM requires the combination of several compatible and complementary practices with the aim to maintain pest populations below the economic injury level (EIL) while reducing the use of high hazardous pesticides and sustaining the action of natural enemies. Several studies have reported the success of IPM in the management of cereals’ stem borers. [25] reported that varietal resistance/tolerance, cultural practices and biological control are important components of integrated management of rice stem borers. Similarly [24] found that the effective control of the African Rice Gall Midge relies on the combination of cultural practices, habitat management and moderate use of insecticide chemical. Kega [47] demonstrated that the use of resistant rice cultivars and entomopathogenic nematodes is a viable method to control M. separatella. Nwanze and Mueller [48] indicated that host plant resistance and cultural practices should be major components in the integrated management of sorghum stem borers. According to [49] an increase of yield can be obtained when sorghum varieties with tolerance or moderate resistance to stem borer are coupled with need-based application of pesticides. Youm et al. [41] suggested options such as early planting, destruction of crop residues and use pheromone bait traps for successful management of the millet stem borer C. ignefusalis. According to the conclusion from an international workshop organized by the International Institute for Semi-Arid Tropics, cultural methods and host plant resistance should be considered as the major components of the integrated management of cereals’ stem borers [50]. However these practices need to be reinforced with other measures such as biological control and if necessary the use of selective systemic chemical . It is important to quote that integrated stem borer management is likely to be severely constrained by the limited capability of farmers to implement several options . For this reason it is highly important that the IPM takes into account the community farming systems and know-how.
4. Conclusion
The stem borers represent a group of insects of economic importance to cereal crops in Africa. Because of the nature of their attacks and the complexity of their biology, the success of the management options will depends on the integration of various strategies ranging from cultural practices to host plant resistance, biological control and moderate use of systemic chemical when necessary. The cultural practices and host plant resistance remains the major component of the IPM of cereal’s stem borers. They can be reinforced by the biological and chemical control. The cultural practices involve farmers’ engagement and cooperation. As for varietal resistance, more research action is needed to identify or develop varieties that tolerate the stem borers attack. Regarding the deployment of chemical and biocontrol options, more intensive action from extension service is need to increase the capacity of farmers so that they can engage appropriate action to limit yield losses in cereal and increase their incomes.
\n',keywords:"IPM, environment friendly, yield loss, biocontrol, cultural practices",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/65296.pdf",chapterXML:"https://mts.intechopen.com/source/xml/65296.xml",downloadPdfUrl:"/chapter/pdf-download/65296",previewPdfUrl:"/chapter/pdf-preview/65296",totalDownloads:975,totalViews:0,totalCrossrefCites:2,totalDimensionsCites:2,totalAltmetricsMentions:1,impactScore:1,impactScorePercentile:60,impactScoreQuartile:3,hasAltmetrics:1,dateSubmitted:"June 5th 2018",dateReviewed:"December 21st 2018",datePrePublished:"January 15th 2020",datePublished:"February 19th 2020",dateFinished:"January 22nd 2019",readingETA:"0",abstract:"The economic importance of the stem borer in Africa results in their severe damage that affect directly cereal yield factors such as the density of fertile tillers and the number of effective panicles. The objective of this paper is to describe and discuss the management options of the main prevalent stem borer of cereal crops in Africa. Host plant resistance, cultural practices, biological control and reasoning chemical control are among the most encouraging options. Integrated pest management combining several compatible methods was highlighted as the most sustainable control option. This paper will served as support for the current research on cereal crops but also as relevant prospect document for entomologists and breeders from across the world.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/65296",risUrl:"/chapter/ris/65296",book:{id:"7545",slug:"pests-control-and-acarology"},signatures:"Abou Togola, Ousmane Boukar, Manuele Tamo and Siva Chamarthi",authors:[{id:"261663",title:"Dr.",name:"Abou",middleName:null,surname:"Togola",fullName:"Abou Togola",slug:"abou-togola",email:"a.togola@cgiar.org",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"274308",title:"Dr.",name:"Ousmane",middleName:null,surname:"Boukar",fullName:"Ousmane Boukar",slug:"ousmane-boukar",email:"o.boukar@cgiar.org",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"274309",title:"Dr.",name:"Manuele",middleName:null,surname:"Tamo",fullName:"Manuele Tamo",slug:"manuele-tamo",email:"m.tamo@cgiar.org",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"274310",title:"Dr.",name:"Siva",middleName:null,surname:"Chamarthi",fullName:"Siva Chamarthi",slug:"siva-chamarthi",email:"s.chamarthi@cgiar.org",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Overview of the main stem borers of cereal crops in Africa",level:"1"},{id:"sec_3",title:"3. Management of the main stem borers of cereal crops in Africa",level:"1"},{id:"sec_3_2",title:"3.1 Preventive cultural practices",level:"2"},{id:"sec_4_2",title:"3.2 Varietal resistance",level:"2"},{id:"sec_5_2",title:"3.3 Biological control measures",level:"2"},{id:"sec_6_2",title:"3.4 Chemical control",level:"2"},{id:"sec_7_2",title:"3.5 Integrated pest management option",level:"2"},{id:"sec_9",title:"4. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Bosque-Pérez NA, Mareck JH. Distribution and species composition of Lepidopterous maize borers in southern Nigeria. Bulletin of Entomological Research. 1990;80(4):363-368'},{id:"B2",body:'Schulthess F, Bosque-Pérez NA, Gounou S. Sampling lepidopterous pests on maize in West Africa. Bulletin of Entomological Research. 1991;81(3):297-301'},{id:"B3",body:'Annecke DP, Moran VC. 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International Institute of Tropical Agriculture, Nigeria
International Institute of Tropical Agriculture, Nigeria
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1. Introduction
Many engineering design problems require simultaneous optimization of multiple, often competing, objectives. Unlike in single-objective optimization, a multi-objective problem with competing objectives has no single solution. An optimum solution with respect to only one objective may not be acceptable when measured with respect to the other objectives. Multi-objective problems have a number of solutions called the Pareto-optimal set, named after Vilfred Pareto [1], that represent the range of best possible compromises amongst the objectives. Traditional gradient-based optimization algorithms are capable of addressing the multi-objective problems by converting the problem into a single-objective formulation. On the other hand, evolutionary algorithms (EAs)1 are well suited for the multi-objective problems as they can evolve to a set of designs that represent the Pareto frontier in a single run of the algorithm [2, 3]. As a result, EAs often find application to address multi-objective problems. Despite the popularity of these algorithms to solve a wide range of problems, they, like all non-gradient meta-heuristic searches, have issues with computational cost and rate of convergence to the Pareto frontier. After some number of generations, the candidate solutions may begin to exhibit little or no improvement. Modified versions of these algorithms exist which improve the convergence rate [4]. However, hybridizing EAs with an efficient gradient-based algorithm may significantly improve the convergence rate and has demonstrated the ability to solve multi-objective problems more efficiently than the EA alone [3]. Hybridization of an EA with a gradient-based local search algorithm has started to gain popularity owing to its promising capabilities to address the demerits of many optimization algorithms when used independently.
The genetic algorithm (GA) [5] is a class of EA and is a well-known population-based global search algorithm. Apart from its ability to explore the design space, GA is also capable of handling both discrete and continuous type design variables. This makes the GA an ideal choice to address problems that combine both discrete and continuous variables. However, the GA, like other EAs, does not provide any proof of convergence, and the GA cannot directly enforce constraints. Commonly, constraint handling for a GA search uses a penalty approach such that the fitness function reflects the objective function value and accounts for violated constraints. This generally requires the use of penalty multipliers to adjust the “strength” with which the penalty impacts the fitness function and selecting suitable penalty multipliers is often difficult. Further, for multi-objective problems, the different scaling or magnitude of the objectives can complicate selecting appropriate penalty multipliers.
On the other hand, Sequential Quadratic Programming (SQP) [6], is a well-known gradient-based search algorithm that directly handles constraints and provides proof of convergence to local optima using Karush-Kuhn-Tucker (KKT) optimality criteria [7]. Because SQP uses gradient information, it is a computationally efficient search algorithm. However, SQP cannot handle discrete design variables or discontinuous functions and has difficulty with multi-modal functions. Therefore, both of these (GA and SQP) well-known optimization algorithms have their own pros and cons that limit their individual applicability to fully address constrained multi-objective problems that combine both continuous and discrete type design variables. Combining the GA with SQP creates a hybrid approach that improves the overall optimization process for constrained mixed-discrete nonlinear programming problems (MDNLP).
The chapter presents a combination of the two-branch tournament GA for multi-objective problems with an SQP-based local search implementation of the goal attainment problem formulation allowing an improved information sharing between the two algorithms. To the best of the authors’ knowledge, there exists no work that emphasizes the process of hybridization combining an N-branch tournament selection GA with the goal attainment formulation as the local search in a compatible manner and then demonstrates application of the approach to solve a hard-to-solve constrained multi-objective, mixed-discrete nonlinear optimization problem. Later in the chapter, the hybrid approach is applied to solve a three-bar truss problem, a ten-bar truss problem, and a greener aircraft design optimization problem – all representatives of constrained multi-objective, mixed-discrete nonlinear programming problem. The truss problems have basis in test problems for structural optimization, and the motivation to select a greener aircraft design optimization problem arises from the increased concern about the environmental impact of the growing air transportation system.
2. Literature review
The ability of the EAs to evolve to a Pareto-frontier as the generation progresses makes them an ideal choice for several multi-objective optimization problems. Vector Evaluated GA (VEGA), proposed by Schaffer [8] back in 1985, is one of the earlier versions of multi-objective GA. Several multi-objective EAs are developed since then including Multi-Objective Genetic Algorithm (MOGA) [9], Strength Pareto Evolutionary Algorithm [10], Non-dominated Sorted Genetic Algorithm (NSGA) [11] to mention a few popular ones.
Coello [2, 12] has conducted comprehensive literature surveys of various evolutionary multi-objective techniques. Konak et al. [13] compared various multi-objective optimization algorithms and provides a set of guidelines to follow while developing a multi-objective algorithm. Their effort primarily lies in guiding researchers with very little background in MOGA and making them familiar with the ideas and approaches of multi-objective optimization.
One such multi-objective algorithm named Non-dominated Sorting Genetic Algorithm (NSGA), developed by Srinivas and Dev [11] – arguably one of the most widely used multi-objective EAs – uses the concept of non-dominated sets originally proposed by Goldberg in his book on Genetic Algorithm and Machine Learning [14]. The NSGA approach maintains sets of non-dominated individuals, with the first set of individuals not dominated by any other individuals in the population. The second set finds the new set of non-dominated individuals after excluding the individuals from the first set. This step continues until all the individuals in the population are categorized inside the non-dominated sets.
A majority of these multi-objective algorithms, in some form, require an assignment of a scalar measure of a fitness value to the individuals in the population. As an example, MOGA [9] and NSGA [11] assign a fitness value based on a ranking scheme depending on the individual’s levels of domination. The two-branch tournament selection genetic algorithm presented by Crossley et al. [15] uses a tournament selection scheme that chooses parents considering both the objectives directly in the fitness functions. The individuals are evaluated based on their fitness across both the objectives. The overall process remains the same as that of a traditional GA. However, the only difference appears in the tournament selection operator. During the tournament selection step, the algorithm selects 50% of the parents based on the fitness value associated with the first objective, that is, the individuals are evaluated solely with respect to the first objective without consideration of the other objective. These selected parents are by nature strong in objective 1, or Φ1-strong. Similarly, the tournament selects the remaining 50% of the parents based on the fitness value associated with the second objective. This second 50% are Φ2-strong parents. With this parent selection approach, randomly choosing the selected parents to pair off for crossover, ideally would result in the following distribution of matches: 25% Φ1−Φ1 type parents, 25% Φ2−Φ2 type parents, and 50% are mixed i.e., Φ1−Φ2 type parents.
The hybrid approach, presented in this chapter, uses this two-branch tournament selection GA as the global search optimizer and combines with a gradient-based approach to refine the search using a novel information sharing concept in the process of hybridization. The unique tournament selection strategy of the two-branch tournament GA allows to understand the underlying trait of the parents, i.e., if they are Φ1 or Φ2 strong, and this information is later leveraged during the crossover step to obtain children with certain desired traits.
Another challenge with multi-objective EAs is their ability to enforce constraints. Unlike gradient-based methods, which use constraint gradient information to guide the search in the feasible direction, no such constraint gradient is available for EAs. There have been several efforts to handle the constraints for EAs; however, not all of these methods strictly or directly enforce the problem constraints. The penalty function approach is arguably the most widely known of the various approaches to handle constraints in EAs. Assuming a minimization problem, this approach adds a penalty to the objective function when constraints are violated [14].
Another simple approach includes ignoring any infeasible design solution; because this does not differentiate between constraints that are close to the constraint boundaries and those that are far apart, this constraint handling method is inefficient.
Binh and Korn [16] suggested a method to assign fitness to individuals based on combining both the objective function vector as well as the degree to which the individual violates the constraint. Infeasible individuals are categorized into different classes based on how close or how far they are to the constraints boundaries.
Fonseca and Fleming [17] proposed a priority-based constraint handling strategy where search is first driven for feasibility followed by optimality by assigning high priority to constraints and low priority to objective functions. Although there are various techniques to “handle” constraints in EAs, “enforcing” them in a robust way is still an open issue. This is another motivation to pursue the hybrid approach that leverages the efficacy of gradient-based search to enforce the problem constraints.
Further, these population-based searches have issues with computational cost and rate of convergence to the Pareto frontier. After some number of generations, the candidate solutions may begin to exhibit little or no improvement. Modified versions of the algorithms work to improve the convergence rate [4, 18]; however, hybridizing EAs or GAs with an efficient gradient-based algorithm can significantly improve the convergence rate, thereby reducing the computational cost. Hybridization of an EA or GA with a gradient-based local search algorithm is not new. There are numerous references demonstrating how hybridization may improve the quality of the search for both single objective and multi-objective problem formulations; these include, but are not limited to, those appearing in [3, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]. The local search can be considered as the local learning that takes place in an individual throughout its lifespan. Some of the approaches apply the local search to the final non-dominated set, while some techniques apply local search to all or many individuals of the population as the generation progresses.
The effort here extends the previous effort by Lehner and Crossley [27] to include a multi-objective formulation and combine the advantage of the hybrid approach with an novel information sharing technique between the global and the local search. The two-branch tournament selection GA algorithm globally explores the design space handling both discrete and continuous type variables, while the gradient-based approach sees only the continuous variables in a goal attainment formulation and seeks to efficiently refine the population based on the information passed on by the top-level GA while enforcing all the problem constraints.
3. Methodology and approach
The hybrid approach presented in this chapter combines the two-branch tournament GA (see Figure 1) for the global search [15] and the goal attainment SQP algorithm provided in the function fgoalattain available from the MATLAB Optimization Toolbox [33] as the local search. For solution via hybrid approach, the problem statement contains two levels, as appears below.
Figure 1.
Original two branch tournament selection GA for two objective problems. Adapted from reference [15].
The top level of the problem, which the GA sees as its optimization problem, is a bound constrained (i.e., only side constraints on the continuous design variables) multi-objective minimization problem that uses the two-branch tournament selection technique with some modification to include the local search. This level includes both discrete and continuous design variables of the original problem. The continuous variables in this level, xc0, are the initial values (starting point) for the local search problem. This way, the GA acts like a guide for a sequential multi-start approach as it searches the combined discrete and continuous design space. The top level formulation appears below:
In the original two-branch tournament selection GA, the tournament step selects 50% of the parents based on the fitness value associated with the first objective. These parents are by nature strong in objective 1 or Φ1-strong. Similarly, the tournament selects the remaining 50% of the parents based on the fitness value associated with the second objective. This second 50% are Φ2-strong parents. With this parent selection approach, randomly choosing the selected parents to pair off for crossover would result, on average, in the following distribution of matches: 25% Φ1-Φ1 type parents, 25% Φ2-Φ2 type parents, and 50% are mixed i.e., Φ1-Φ2 type parents. This has the effect of generating many compromise solutions near the middle of the Pareto frontier, potentially limiting the spread and quality of the Pareto-front. The approach described in this chapter improves the spread and quality of the Pareto front by pairing off the parents in a more prescribed manner. A flowchart depicting how the modified two-branch tournament GA interacts with the gradient-based (SQP) for local search appears in Figure 2.
Figure 2.
Modified two branch tournament selection GA and SQP interaction.
With a given goal fiG, a starting point for the continuous variables xc0 and a set of discrete values xd, the goal attainment problem formulation, for each individual in the GA-level population, seeks to find the optimal design xc∗. The goal attainment problem formulation also assigns the fitness value to the individuals, thereby waiving off the need of fitness evaluation at the GA-level (level I). Using the fitness information of these populations, a new set of goals are generated for the next iteration following the tournament selection, crossover and mutation steps of the two-branch tournament GA algorithm. The resulting design xc∗ need not conform to the binary Gray coding scheme implemented to represent the chromosome of each individual in the population. The effort here employs a Lamarckian strategy [20], that updates the chromosomes of the individual to conform to the gray-coding scheme of the GA.
Figure 3 demonstrates the parent selection process of the new two-branch tournament selection GA and the goal assignment technique with a simple example. The approach starts with a population size of 8n, where n is any positive integer (Figure 3 assumes a population size of 8; i.e., n=1). After the two-branch tournament selection process, 4n parents are Φ1-strong and the other 4n parents are Φ2-strong. These parent groups remain in two separate parent pools. An additional step after the two branch selection process further categorizes these parents into sub-pools to ensure a prescribed mix of Φ1-strong and Φ2- strong parents for crossover. To begin, half of the parents from pool 1 (which contains Φ1-strong parents) are randomly moved to sub-pool 1. This sub-pool contains Φ1-Φ1 type parents paired for crossover and leads to offspring that will likely be Φ1-strong. Similarly, half of the parents from pool 2 are randomly moved to sub-pool 2 to form Φ2-Φ2 type paired parents. Sub-pool 3 pairs parents so that a Φ1-strong parent and a Φ2-strong parent form children via the crossover operation. These would create children that have features from both Φ1 and Φ2 strong parents. This modification to the original two-branch tournament selection approach leads to a more prescribed, yet diversified, set of parents in each pool for the crossover, somewhat analogous to the idea of breeding for plant hybridization.
Figure 3.
Selective parent mixing strategy.
3.2 Level II: sequential quadratic programming
The lower-level problem presented to the SQP algorithm refines the population of the GA by searching the continuous variable space and helps the hybrid algorithm converge to the Pareto frontier at a faster rate. The fgoalattain algorithm, available in MATLAB, converts the multi-objective algorithm into a single-objective optimization problem by converting all the objectives into a set of inequality constraints and minimizes a slack variable γ (also called the attainment factor) as the objective.
This goal attainment formulation seeks to attain values for the objectives close to a set of predefined goal values, fiG, without violating any of the problem constraints gix≤0 and hkx=0. The weight values, αi, are set as the absolute of the corresponding goal values, fiG, based on the guidance in [34]. This prevents scaling issues with objectives of various dimensions and magnitudes. The solution to this problem describes the set of continuous variables xc∗ that minimizes γ and satisfies all constraints; the values of fixc∗–the fitness value of the individual in the population–are returned to the GA-level for the use in the two-branch tournament selection.
The fgoalattain formulation needs a defined goal point in the objective space, and the algorithm tries to find a design as close as possible to these goal values. Figure 4 illustrates the goal point assignment task for each newly created individual in the sub-pools following the example presented in Figure 3. In Figure 4, the points indicate the child “designs” from a set of parents; e.g., C11−4 is the first child from the crossover of parent 1 and parent 4. The color of the symbol indicates the parent sub-pool from which the child designs were generated. Therefore, in this example, there are two children generated from parents 1 and 4 in sub-pool 1, which are indicated with the light blue color to match Figure 3. There are four children generated from sub-pool 2 and two children from sub-pool 3.
Figure 4.
Goal assignment technique.
To assign the goal point values, the hybrid approach first identifies the local ideal point in each generation. This ideal point is the combination of the lowest f1 and f2 values in the current population. For this effort, the utopia point (which includes some tolerance to give the utopia smaller–or better–f1 and f2 values than the ideal point with the intent of encouraging under achievement in the goal attainment problem) is set as 0.95 times the local ideal point. In subsequent generations, any new objective value smaller than the corresponding value in the current utopia point replaces that current value in the utopia point. This makes the utopia point dynamic with each generation. For two-objective problems, two perpendicular lines originate from the utopia point and extend infinitely into the objective space. These straight lines appear as dashed lines in Figure 4.
To assign a goal point to an individual, the approach defines a vector that originates from an individual and ends to where the vector intersects with either of the dotted lines. The point of intersection becomes the goal point for that individual. Children of parents from sub-pool 1, the Φ1-strong sub-pool, receive a goal vector with slope of zero in the objective space. These are the horizontal arrows in Figure 4. These goal points would seek the most improvement along the direction of objective 1. Similarly, children of parents from sub-pool 2 receive a goal vector with 90 degree slope in the objective space. This ensures improvement along the direction of objective 2. Lastly, the children from sub-pool 3 parents receive goal vectors relative to their spatial location in the objective space. An individual closer towards objective 1 will have a vector inclined more towards improvement in objective 1 and vice versa.
Referring back to Figure 3, parents 1 and 4 from sub-pool 1 create children C11−4 and C21−4. This indicates the first child of parent 1 and 4 and the second child of parent 1 and 4 respectively. During the SQP search, these children have goal points that will minimize along the direction of f1 without increasing their current values of f2. C15−7 and C25−7 result from sub-pool 2, and the local search will seek to improve f2 without increasing f1. C13−6, C23−6, C12−8 and C22−8 all result from sub-pool 3, and they will have different goal points for their local searches to improve both f1 and f2. This modified parent selection and goal assignment strategy, via the hybrid formulation, seeks to exploit the tournament selection process of the two-branch tournament GA and tailor the local search for children, depending on traits of their parents.
Although the approach seems robust in enforcing constraints via goal attainment formulation, there may be instances when no feasible solution exists to the goal attainment formulation for a given set of discrete variables. In such cases, the local search will not be able to return a feasible solution and the fitness function receives a severe penalty in the GA-level in an effort to discard such discrete design choices from the population. This severe penalty has some resemblance to the approach of ignoring infeasible designs that was criticised above; however, because the situation where no locally-feasible design exists results from a specific combination of discrete variables, there is no analog to having a “nearly feasible” design with a slightly violated constraint. Severely penalizing such infeasible designs for certain combinations of discrete variable choices, in this context, is appropriate.
4. Application to engineering design problems
To demonstrate the efficacy of the hybrid approach in solving constrained multi-objective MDNLP problems, we solve three different engineering test problems with varying difficulties - a three-bar truss, a ten-bar truss, and greener aircraft design problem.
4.1 Three-bar truss problem
For the three-bar truss problem (see Figure 5), the problem formulation includes the objectives of minimizing the weight of the truss and minimizing the deflection of the free node. The deflection of a node is calculated as the resultant of the deflections in both the x and y directions. The problem consists of six design variables, of which three are continuous and three are discrete. The continuous variables describe the cross-sectional area of the three bars while the discrete variables describe the material selection properties of these bars. The details of the continuous design variables and their design bounds appear in Table 1. For this problem, four discrete material selection choices are available for each element and include aluminum, titanium, steel, and nickel options. The yield stress for every bar acts as a constraint for the problem (total three constraints), not allowing the stress in the bar to go beyond that upper limit. References [35, 36] provide more details about the three-bar truss problem. For the hybrid approach, the GA population is limited to 8 individuals while setting the upper limit for the number of generations to 50. The probability of crossover is set to 0.5 and the mutation rate is fixed at 0.005. The continuous and discrete variables uses 8 and 2 bits respectively in the Gray-coded binary scheme.
Figure 5.
Three bar truss problem.
Design variables
Lower bound
Upper bound
Cross-sectional area of bar 1 [cm2]
0
5
Cross-sectional area of bar 2 [cm2]
0
5
Cross-sectional area of bar 3 [cm2]
0
5
Table 1.
Continuous variables for three-bar truss problem.
The resulting Pareto frontier for the three-bar truss problem appears in Figure 6(a). The plot shows the Pareto frontier has a good spread, leading to a total of 248 non-dominated points as solutions to the optimization problem. The visible trend in the non-dominated design set indicates that as the weight of the three bar truss system increases, they are accompanied by similar increases in the cross-sectional area of the bars with the material selection choice gradually shifting to steel for all the three bars. Aluminum or nickel never appeared as the material selection choice in the first two bars. The designs visible in the top left corner of the Pareto front in Figure 6(a) correspond to high displacement and low weight designs. The separated cluster of points (six designs) visible at the bottom right corner of the Pareto frontier corresponds to low displacement and high weight designs, with the maximum weight design having a material combination of all steel bars.
Figure 6.
Pareto front for the three-bar truss problem and its comparison with the other approaches. (a) Pareto front for the three-bar truss problem using the hybrid approach. (b) Comparison of Pareto frontier obtained using the hybrid approach, a weighted sum approach and the original two-branch tournament GA approach.
For the three-bar truss problem, only 64 possible combinations of discrete design variables exist. Hence, it is possible to perform a complete enumeration of the discrete design space and get a sense of the shape of the true Pareto front and help assess the performance of the hybrid approach. This led the authors to compare the hybrid approach (and the original two-branch tournament selection GA2) with a gradient-based weighted sum approach for this three-bar truss problem. The weighted sum approach converts the multi-objective problem formulation into a single objective problem by assigning weights to both the objectives and solves the single objective problem with the gradient-based approach using MATLAB’s fmincon solver [33].
First, the objectives are normalized using the utopia point. Next, objective 1 is assigned a weight w that varies from 0 to 1 in a step increment of 0.05. The weight for the second objective is set to 1−w. For each possible combination of discrete variable choice and a given weight pair, the approach leads to a single point in the objective space. The weighted sum approach then conducts gradient-based search for all 21 different weight pairs corresponding to each of the 64 possible discrete combination choices. The resulting Pareto frontier using the weighted sum approach is compared with the hybrid approach and the original two-branch tournament GA approach in Figure 6(b). The original two-branch tournament GA finds an inferior set of solutions, possibly due to the lack of local search feature, and the set of solutions also has a reduced spread across the Pareto frontier. On the other hand, the weighted sum approach with complete enumeration on the material selection choices has a slightly better spread compared to the hybrid approach but with fewer non-dominated points.
Figure 7 compares how the Pareto frontier evolved with generations using the original two-branch tournament GA and the proposed hybrid approach. As expected, without the local search feature, the original two-branch tournament selection GA shows distinct improvement in both the quality and the spread of the Pareto front as the generation progresses. That is, the black diamonds (non-dominated set after second generation) are replaced with better non-dominated designs as the generation progresses. However, in the hybrid case, we start to see the shape of the final Pareto front immediately after the second generation. As the generation progresses further, more points get added to the list of non-dominated designs. This is due to the multi-start approach where the top-level GA populates various possible combinations of the discrete material selection choices and the local gradient-based search then improves these designs by varying the continuous design variables. The hybrid approach is able to rapidly get to the final Pareto front at the expense of increased number of function evaluations needed by the gradient-based local search.
Figure 7.
Evolution of the non-dominated sets as the generation progresses. (a) Original two-branch tournament selection GA. (b) Proposed hybrid approach.
4.2 Ten-bar truss problem
Next, the hybrid approach solves a more difficult and challenging version of the three-bar truss problem – a ten-bar truss. Similar to the three-bar truss problem, the ten-bar truss has the competing objectives that include minimizing the weight of the ten-bar truss system and minimizing the resultant displacement of any of the free nodes. The displacement is taken as the absolute of the maximum calculated displacement among all the bar elements. This problem consists of twenty design variables – ten continuous type and ten discrete type. The continuous variables describe the cross-sectional diameters of the ten bars, ranging from 0.1 cm2 to 40 cm2, while the discrete variables specify the material selection properties of these bars. Like the three-bar problem, the four discrete material choices available for each bar include aluminum, titanium, steel, and nickel. However, this problem has over one million possible combinations of the discrete choices (410=1,048,576) making complete enumeration of the discrete design space computationally prohibitive, unlike the three-bar truss. References [35, 36] provide more details about the ten-bar truss problem considered in this study.
Figure 8(a) compares the Pareto front obtained using the hybrid approach after 20 GA generations with the Pareto frontier obtained using the two-branch tournament selection GA after 100 generations. The figure shows both the approaches performed well for this problem with the two-branch tournament selection GA resulting a better spread in the low weight/high displacement region of the objective space, whereas the hybrid GA has a better spread in the low displacement/high weight region. Figure 8(b) shows how the non-dominated set evolved as the generation progresses using the hybrid approach. We see a similar trend as that of the three-bar truss problem. That is, there is not much significant change in the final shape of the Pareto front other than the increase in the number of non-dominated designs as the generation progresses. However, this time there is slight improvement in the quality of the Pareto front (the red non-dominated set obtained after generation 20 is slightly better than the blue or the black non-dominated designs obtained at generation 5 and 2 respectively).
Figure 8.
Ten bar truss problem results. (a) Comparison between the original two-branch tournament GA (after 100 GA generation) and the hybrid approach (after 20 GA generations) for the 10 bar truss problem. (b) Evolution of non-dominated set as the generation progresses for the 10 bar truss problem using the hybrid approach.
For the three-bar example, a majority of the improvements across the objective space are due to the gradient-based local search’s ability to obtain designs with better cross-sectional area. With only 64 possible material selection combinations, there are not many discrete material selection options to explore. On the other hand, for the ten-bar truss problem, a vast majority of the improvement is due to the ability of the GA to find a better material selection combination rather than fine-tuning the cross sectional variables. It is not possible to seek further improvement in the Pareto front just by varying the continuous variables, so the local search saturates as appear in the case of black (diamonds) and blue (squares) non-dominated designs. After few more GA iterations, the algorithm is able to find better combinations of material selection that lead to further improvement in the Pareto front (red dots).
4.3 Greener aircraft design problem
The third application problem solved using the hybrid approach is the greener aircraft design problem. Here, a “greener” aircraft design problem provides an example to demonstrate the efficacy of the hybrid algorithm and its ability to solve such MDNLP problems. The intent is to find aircraft designs that represent the best possible trade-offs among performance, economics, and environmental metrics which essentially makes this a multi-objective problem. Further, with the inclusion of discrete technologies, the problem becomes MDNLP in nature.
The aircraft design optimization problem employs the NASA sizing code FLOPS [37] to evaluate discrete design configurations and perform the sizing and performance calculations of the candidate aircraft designs. The sizing code accepts both continuous and discrete design variables as input and returns the aircraft gross weight along with environmental metrics (fuel weight, which corresponds to CO2 emissions, and NOX emissions) and total operating cost. Simple models simulating the potential “greener technologies” are modeled in MATLAB [33] and then integrated with FLOPS for the performance calculations. The goal of the aircraft sizing problem is to develop an aircraft with 2940 nmi design range with a seat capacity of 162 seats in two classes. A brief description of the greener aircraft design optimization problem appears below. For more details about the aircraft design problem, we encourage the readers to see Ref. [38].
4.3.1 Description of the continuous variables
The problem includes ten continuous variables that define the wing and the engine parameters of the aircraft. The details of these continuous design variables and their design bounds appear in Table 2.
Design variables
Lower bound
Upper bound
Aspect Ratio
8
12
Taper Ratio
0.3
0.5
Thickness to Chord Ratio
0.09
0.17
Wing Area [ft2]
1,000
1,500
Wing Sweep at 25 percent [deg]
0
40
Thrust per engine [lbs]
20,000
30,000
By-Pass Ratio
5
10
Turbine Inlet Temperature [R]
3010
3510
Overall Pressure Ratio
35
55
Fan Pressure Ratio
1.6
1.7
Table 2.
Continuous variables for aircraft design problem.
4.3.2 Simulating the discrete technologies
This aircraft design optimization study models three types of discrete technologies. Table 3 lists the set of discrete technologies considered in this study. To model composite material selection choice on various aircraft components, the approach here uses a binary variable for each of the aircraft components that includes wing, fuselage, tail, and nacelle. A value of one represents composites being present while a value of zero represents no composite materials in that structure. The second discrete variable includes the eight possible combinations of the location and the number of engines. Lastly, eight combinations of laminar flow technologies are included for this problem, depending on whether it is natural laminar flow (NLF) or hybrid laminar flow control (HLFC) technology and the number of components on which it is applied (as listed in Table 3). References [38, 39, 40] describe the various discrete technologies used in this study in further detail.
Laminar Flow Technologies
Engine Position
Composite Material Choices
Wing
Fuselage
Nacelle
Tail
NLF-Wing
2 wing
Yes
Yes
Yes
Yes
HLFC-Wing
2 fuselage
No
No
No
No
HLFC-Wing + Nacelle
2 wing +1 fuselage
HLFC-Wing + Tail
3 fuselage
HLFC-Wing + Tail + Nacelle
4 wing
NLF-Wing + HLFC-Tail
2 wing +2 fuselage
NLF-Wing + HLFC-Nacelle
1 fuselage
NLF-Wing + HLFC-Tail + HLFC-Nacelle
4 wing +1 fuselage
Table 3.
Discrete technologies for aircraft design problem.
The problem also has four constraints that appear in Table 4. The constraints ensure that the design solution meets the desired field length criteria, has sufficient ground clearance, and sets a maximum limit on the amount of allowable fuel carrying space in the fuselage.
Take-off field length [ft]
≤ 8,000
Landing field length [ft]
≤ 7,500
Landing gear length [in]
≤ 150
Fuselage fuel capacity [lbs]
≤ 28,800
Table 4.
Problem constraints.
The aircraft design optimization problem considers two different pairs of competing objectives. The first pair involves simultaneous minimization of the aircraft fuel weight (index of CO2 emissions) and the total operating cost of the aircraft, and the second pair involves minimizing the NOX emissions and the total operating cost of the aircraft. The GA population has been limited to 48 individuals while setting the upper limit for the number of generations to 50 as before. The maximum number of function evaluations for the SQP minimization (using MATLAB’s fmincon) have been limited to the default value of 100 times the total number of continuous variables for this study. For certain combinations of discrete technology selection choices, the gradient-based approach may not find a feasible solution. In such cases, as mentioned in the methodology section, those designs are assigned high penalty for elimination in the subsequent generations.
4.3.3 Results for aircraft design problem
4.3.3.1 Objective pair - fuel weight vs. total operating cost.
Figure 9 shows the set of 24 non-dominated designs for the competing objective pair – aircraft fuel weight and total operating cost. The aircraft fuel weight, analogous to fuel burn, is directly related to the amount of CO2 produced during the trip. The Pareto frontier consists of designs employing combinations of composite structures, eight different engine position(s), and eight different laminar flow technologies, modeled as a part of the greener technology approaches described in the previous section.
Figure 9.
The non-dominated set for objective pair – aircraft fuel weight (index for CO2 production) and the total operating cost.
The design point ND1 (for Non-Dominated design number 1) in Figure 9 corresponds to highest total operating cost (also lowest fuel weight) and makes use of NLF technology on the wing and HLFC technology on the nacelles and tail, along with two wing-mounted engines. This design also features composite wings, fuselage, and nacelles. The use of composite structures leads to a decrease in the fuel consumption (due to the reduction in aircraft empty weight) at the expense of increased total operating cost (due to increase in the manufacturing and maintenance costs associated with composite materials). The design with the lowest total operating cost (ND24) makes use of NLF technology on the wing and HLFC technology on the nacelles and tail, along with two wing-mounted engines as well. But, this design has no composite components and, hence, has the lowest total operating cost according to the models used in this study.
All the non-dominated designs employ NLF technology on the wings and HLFC technology on both the nacelle and tail, along with two wing-mounted engine configuration. The laminar flow technologies tend to reduce the skin friction drag of the aircraft, making the design more aerodynamically efficient, and reducing its fuel consumption for a given mission range. All the non-dominated designs employ these technologies in various forms (NLF or HLFC) to reduce fuel burn, depicting the importance of employing these technologies in near future “greener” aircraft design.
An interesting region in the Pareto frontier from an airline’s standpoint would be near the points ND1 and ND3 (or ND2), where a substantial decrease in total operating cost is possible for a marginal increase in the aircraft fuel weight (index of CO2 production per trip). Considering non-dominated designs ND1 and ND3, a nearly 1% reduction in total operating cost is possible to achieve for only a 0.6% increase in the total fuel weight needed for the mission, as one move from ND1 to ND3. Similar trends for the objective pair in consideration are also observed for designs ND9, ND10, and ND11.
4.3.3.2 Objective pair - NOX emissions and total operating cost
The Pareto front corresponding to the NOX emissions and the total operating cost objective pair appears in Figure 10 and has 24 non-dominated designs. The non-dominated designs have different geometric design variable values that best match the different discrete greener aircraft technologies to arrive at the trade-off between the NOX emissions and the total operating cost.
Figure 10.
The non-dominated set for objective pair – NOX emissions versus total operating cost.
The design with minimum NOX emissions and maximum total operating cost (ND1) employs a three-engine configuration with one fuselage-mounted and two wing-mounted engines, along with a composite wing. The laminar flow technologies on this design include NLF technology on wings and HLFC technology on the nacelles and tail. The maximum NOX emitting design with minimum total operating cost (ND24) employs a two-engine configuration with wing-mounted engines, along with NLF technology on the wings and HLFC technology on the tail, and a composite nacelle. All the non-dominated designs, except the one with maximum NOX emissions, employ NLF technology on the wings and HLFC technology on both the nacelle and tail. As we move from left to right along the Pareto frontier in Figure 10, the aircraft engines change from a three-engine configuration (two wing-mounted and one fuselage-mounted) to a two-engine (wing-mounted) configuration, thereby reducing the total operating cost.
An interesting region from the airline’s point of view is the near the points ND2, ND3, ND4 and ND5, where a nearly vertical portion is visible in the top left portion of the Pareto frontier (refer to Figure 10). Moving from left to right in this region, a substantial decrease in total operating cost is possible for a marginal increase in the NOX emissions of the aircraft. A plausible design from an airline’s perspective–among the obtained non-dominated designs–would be the ND10 design. The reason for this observation is that a substantial increase in total operating cost will be incurred if further reduction in NOX emissions are desired, while any effort to further reduce the total operating cost will lead to very high NOX emissions, which is not desired from an environment standpoint.
Given there is some degree of randomness associated with the genetic operations in the GA, subsequent runs of the hybrid GA for the two objective pairs find a slightly different number of non-dominated designs points. However, the basic trait of the Pareto frontier, in terms of the discrete choices, did not alter; only the density of points in the Pareto frontier varied with different runs.
5. Conclusions
This chapter describes a hybrid multi-objective algorithm that makes use of an efficient gradient-based SQP algorithm for fitness evaluation inside a GA in a learning approach. The combination allows the GA to evolve a population of designs in the direction of the Pareto frontier while the SQP algorithm enforces constraints, eliminating the need for penalty multipliers or other special constraint handling methods and refines the values of the continuous design variables. The selective parent mixing and unique sets of goal point assignment to the individual lead to a distinct improvement in convergence and the quality of the Pareto frontier from a previous variation of this approach. When applied to various constrained MDNLP engineering design problems, the hybrid algorithm shows the ability to identify promising designs.
Although the ability of the hybrid approach to solve difficult constrained MDNLP problems is demonstrated in this chapter, the methodology relies heavily on the constraint enforcing ability and efficient searching of the continuous design space via the local gradient-based SQP algorithm that requires some estimates (either numerically or analytically) of the gradients of the objectives and the constraints with respect to the continuous design variables. A major advantage of a gradient-based approach besides being able to enforce the problem constraints (hence, the motivation to hybridize) is that the computational cost needed to compute the gradients is nearly independent of the number of design variables [41] when using adjoint-based methods to estimate the derivatives. This allows the gradient-based approach to efficiently solve problems with a very large number of design variables. However, if the objectives are encapsulated in a black-box function and are computationally very expensive to evaluate, then it may not be possible to directly implement a gradient-based search and may require a surrogate-based design optimization approach [40, 42, 43].
Nomenclature
αi
Weight vector for the relative under/over-attainment of objective
fi(x)
Value of the objective
fiG
Goal value for objective
gi(x)
Nonlinear inequality constraints
γ
Attainment factor
hi(x)
Nonlinear equality constraints
n
Population size
xc
Continuous design variable
xd
Discrete design variable
xL
Design variable lower bound
xU
Design variable upper bound
EA
Evolutionary algorithm
GA
Generic algorithm
HLFC
Hybrid laminar flow control
MDNLP
Mixed-discrete nonlinear programming
ND
Non-dominated design
NLF
Natural laminar flow
NSGA
Non-dominated Sorted Genetic Algorithm
SPEA
Strength Pareto Evolutionary Algorithm
SQP
Sequential Quadratic Programming
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This chapter presents a novel hybrid approach to find solutions to a constrained multi-objective mixed-discrete nonlinear programming problem that combines a two-branch genetic algorithm as a global search tool with a gradient-based approach for the local search. Hybridizing two algorithms can provide a search approach that outperforms the individual algorithms; however, hybridizing the two algorithms, in the traditional way, often does not offer advantages other than the computational efficiency of the gradient-based algorithms and global exploring capability of the evolutionary-based algorithms. The approach here presents a hybridization approach combining genetic algorithm and a gradient-based approach with improved information sharing between the two algorithms. The hybrid approach is implemented to solve three engineering design problems of different complexities to demonstrate the effectiveness of the approach in solving constrained multi-objective mixed-discrete nonlinear programming problems.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/76330",risUrl:"/chapter/ris/76330",signatures:"Satadru Roy, William A. Crossley and Samarth Jain",book:{id:"10627",type:"book",title:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",subtitle:null,fullTitle:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",slug:null,publishedDate:null,bookSignature:"Dr. Marcos Sales Guerra Tsuzuki and Prof. Rehab O. O. 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Application to engineering design problems",level:"1"},{id:"sec_6_2",title:"4.1 Three-bar truss problem",level:"2"},{id:"sec_7_2",title:"4.2 Ten-bar truss problem",level:"2"},{id:"sec_8_2",title:"4.3 Greener aircraft design problem",level:"2"},{id:"sec_8_3",title:"Table 2.",level:"3"},{id:"sec_9_3",title:"Table 3.",level:"3"},{id:"sec_10_3",title:"4.3.3 Results for aircraft design problem",level:"3"},{id:"sec_10_4",title:"4.3.3.1 Objective pair - fuel weight vs. total operating cost.",level:"4"},{id:"sec_11_4",title:"4.3.3.2 Objective pair - NOX emissions and total operating cost",level:"4"},{id:"sec_15",title:"5. Conclusions",level:"1"},{id:"sec_18",title:"Nomenclature",level:"1"}],chapterReferences:[{id:"B1",body:'Cirillo R. The Economics of Vilfredo Pareto. Routledge; 1979. ISBN: 978-1-136-27816-7'},{id:"B2",body:'Coello Coello C A. A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques. Knowledge and Information Systems 1, 1999;269–308. 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Hybrid Non-dominated Sorting Differential Evolutionary Algorithm with Nelder-Mead. Second WRI Global Congress on Intelligent Systems. 2010;1:306-311. DOI: 10.1109/GCIS.2010.198'},{id:"B29",body:'Wang X, Tang L. A PSO-Based Hybrid Multi-Objective Algorithm for Multi-Objective Optimization Problems. In: Tan Y, Shi Y, Chai Y, Wang G, editors. Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729, Berlin, Heidelberg: Springer; 2011. DOI: 10.1007/978-3-642-21524-7_4'},{id:"B30",body:'Li X, Du G. BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems. Computers & Operations Research. 2013;40:282-302. DOI: 10.1016/j.cor.2012.07.014'},{id:"B31",body:'Žilinskas A, Žilinskas J. A hybrid global optimization algorithm for non-linear least squares regression. Journal of Global Optimization. 2013;56:265-277. DOI: 10.1007/s10898-011-9840-9'},{id:"B32",body:'Lohpetch D, Jaengchuea S. 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Purdue University; 2018'},{id:"B37",body:'McCullers L A, FLOPS, Software Package, Ver. 8.12 NASA Langley Research Center. Hampton, VA 2010'},{id:"B38",body:'Lehner S, Crossley W. A. Combinatorial Optimization to Include Greener Technologies in a Short-to-Medium Range Commercial Aircraft In: The 26th Congress of ICAS and 8th AIAA ATIO DOI: 10.2514/6.2008-8963'},{id:"B39",body:'Roy S, Crossley W A. Hybrid Multi-Objective Combinatorial Optimization Technique with Improved Compatibility between GA and Gradient-Based Local Search. In: Proceedings of 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference; September 2012. AIAA; 2012'},{id:"B40",body:'Jain S, Crossley W A, Roy S. A Multi-Fidelity Approach to Address Multi-Objective Mixed-Discrete Nonlinear Programming Problems. In: 2018 Multidisciplinary Analysis and Optimization Conference; June 2018. AIAA; 2018'},{id:"B41",body:'Gray J S, Hwang J T, Martins J R R A, Moore K T, Naylor B A. OpenMDAO: An open-source framework for multidisciplinary design, analysis, and optimization. Structural and Multidisciplinary Optimization. 2019;59:1075-1104. DOI: 10.1007/s00158-019-02211-z'},{id:"B42",body:'Jones D R, Schonlau M, Welch W J. Efficient Global Optimization of Expensive Black-Box Functions. Journal of Global Optimization. 1998;13:455-492. DOI: 10.1023/A:1008306431147'},{id:"B43",body:'Roy S, Crossley W A, Moore K T, Gray J S, Martins J R R A. Monolithic Approach for Next-Generation Aircraft Design Considering Airline Operations and Economics. Journal of Aircraft. 2019;56:1565-1576. DOI: 10.2514/1.C035312'}],footnotes:[{id:"fn1",explanation:"Here, the term “evolutionary algorithm” encompasses all population-based search algorithms that use features inspired by biological evolution."},{id:"fn2",explanation:"The original two-branch tournament selection GA was proposed for unconstrained problems. In this example, the problem constraints in the original two-branch tournament selection GA (used for comparison) are handled using an exterior penalty approach."}],contributors:[{corresp:null,contributorFullName:"Satadru Roy",address:null,affiliation:'
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There are many hybrid techniques, which arise from CVD and are constantly evolving in order to modify the properties of the fabricated thin films. Amongst them, plasma enhanced chemical vapor deposition (PECVD) is a technique that can extend the applicability of the method for various precursors, reactive organic and inorganic materials as well as inert materials. Organic/inorganic monomers, which are used as precursors in the PECVD technique, undergo disintegration and radical polymerization while exposed to a high-energy plasma stream, followed by thin film deposition. In this chapter, we have provided a summary of the history, various characteristics as well as the main applications of PECVD. By demonstrating the advantages and disadvantages of PECVD, we have provided a comparison of this technique with other techniques. PECVD, like any other techniques, still suffers from some restrictions, such as selection of appropriate monomers, or suitable inlet instrument. 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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:null,scope:"
\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
\r\n
\r\n\t
\r\n
\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
\r\n
\r\n\t
\r\n
\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
\r\n
\r\n\t
\r\n
\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
\r\n
\r\n\t
\r\n
\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
\r\n
\r\n\t
\r\n
\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
\r\n
\r\n\t
\r\n
\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
\r\n
\r\n\t
\r\n
\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"April 24th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:0,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. 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Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:{title:"Infectious Diseases",id:"6"},selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. This series is intended for doctors, engineers, and scientists involved in biomedical engineering or those wanting to start working in this field.",coverUrl:"https://cdn.intechopen.com/series/covers/7.jpg",latestPublicationDate:"May 7th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:3,numberOfPublishedChapters:96,numberOfPublishedBooks:12,editor:{id:"50150",title:"Prof.",name:"Robert",middleName:null,surname:"Koprowski",fullName:"Robert Koprowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTYNQA4/Profile_Picture_1630478535317",biography:"Robert Koprowski, MD (1997), PhD (2003), Habilitation (2015), is an employee of the University of Silesia, Poland, Institute of Computer Science, Department of Biomedical Computer Systems. For 20 years, he has studied the analysis and processing of biomedical images, emphasizing the full automation of measurement for a large inter-individual variability of patients. Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. Osma",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDv7QAG/Profile_Picture_1626602531691",institutionString:null,institution:{name:"Universidad de Los Andes",institutionURL:null,country:{name:"Colombia"}}},{id:"69697",title:"Dr.",name:"Mani T.",middleName:null,surname:"Valarmathi",fullName:"Mani T. Valarmathi",profilePictureURL:"https://mts.intechopen.com/storage/users/69697/images/system/69697.jpg",institutionString:"Religen Inc. | A Life Science Company, United States of America",institution:null},{id:"205081",title:"Dr.",name:"Marco",middleName:"Vinícius",surname:"Chaud",fullName:"Marco Chaud",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDGeQAO/Profile_Picture_1622624307737",institutionString:null,institution:{name:"Universidade de Sorocaba",institutionURL:null,country:{name:"Brazil"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/342628",hash:"",query:{},params:{id:"342628"},fullPath:"/profiles/342628",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()