To purchase hard copies of this book, please email:
orders@intechopen.com
By only printing on demand InTech ensures our carbon footprint is kept to a minimum.
The data below shows the environmental impact of printing one single book:
76.77 kg wood
4.13 g CO2
69.7 ml Water
Share this page
Swarm Intelligence, Focus on Ant and Particle Swarm Optimization
Edited by Felix T.S. Chan and Manoj Kumar Tiwari, ISBN 978-3-902613-09-7, Hard cover, 532 pages, Publisher: I-Tech Education and Publishing, Published: December 01, 2007 under CC BY-NC-SA 3.0 license, in subject Numerical Analysis and Scientific Computing
DOI: 10.5772/48
In the era globalisation the emerging technologies are governing engineering industries to a multifaceted state. The escalating complexity has demanded researchers to find the possible ways of easing the solution of the problems. This has motivated the researchers to grasp ideas from the nature and implant it in the engineering sciences. This way of thinking led to emergence of many biologically inspired algorithms that have proven to be efficient in handling the computationally complex problems with competence such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), etc. Motivated by the capability of the biologically inspired algorithms the present book on "Swarm Intelligence: Focus on Ant and Particle Swarm Optimization" aims to present recent developments and applications concerning optimization with swarm intelligence techniques. The papers selected for this book comprise a cross-section of topics that reflect a variety of perspectives and disciplinary backgrounds. In addition to the introduction of new concepts of swarm intelligence, this book also presented some selected representative case studies covering power plant maintenance scheduling; geotechnical engineering; design and machining tolerances; layout problems; manufacturing process plan; job-shop scheduling; structural design; environmental dispatching problems; wireless communication; water distribution systems; multi-plant supply chain; fault diagnosis of airplane engines; and process scheduling. I believe these 27 chapters presented in this book adequately reflect these topics.
This book is indexed in:
Book contents
- Chapter 1Chaotic Rough Particle Swarm Optimization Algorithms
- Chapter 2Power Plant Maintenance Scheduling Using Ant Colony Optimization
- Chapter 3Particle Swarm Optimization for Simultaneous Optimization of Design and Machining Tolerances
- Chapter 4Hybrid Optimisation Method for the Facility Layout Problem
- Chapter 5Selection of Best Alternative Process Plan in Automated Manufacturing Environment: An Approach Based on Particle Swarm Optimization
- Chapter 6Job-shop Scheduling and Visibility Studies with a Hybrid ACO Algorithm
- Chapter 7Particle Swarm Optimization in Structural Design
- Chapter 8Reserve-Constrained Multiarea Environmental/Economic Dispatch Using Enhanced Particle Swarm Optimization
- Chapter 9Hybrid Ant Colony Optimization for the Channel Assignment Problem in Wireless Communication
- Chapter 10Case Study Based Convergence Behaviour Analysis of ACO Applied to Optimal Design of Water Distribution Systems
- Chapter 11A CMPSO Algorithm Based Approach to Solve the Multi-plant Supply Chain Problem
- Chapter 12Ant Colonies for Performance Optimization of Multi-components Systems Subject to Random Failures
- Chapter 13Distributed Particle Swarm Optimization for Structural Bayesian Network Learning
- Chapter 14CSV-PSO and Its Application in Geotechnical Engineering
- Chapter 15Application of PSO to Design UPFC-based Stabilizers
- Chapter 16Integration Method of Ant Colony Algorithm and Rough Set Theory for Simultaneous Real Value Attribute Discretization and Attribute Reduction
- Chapter 17A New Ant Colony Optimization Approach for the Degree-Constrained Minimum Spanning Tree Problem Using Pruefer and Blob Codes Tree Coding
- Chapter 18Robust PSO-Based Constrained Optimization by Perturbing the Particle's Memory
- Chapter 19Using Crowding Distance to Improve Multi-Objective PSO with Local Search
- Chapter 20Simulation Optimization Using Swarm Intelligence as Tool for Cooperation Strategy Design in 3D Predator-Prey Game
- Chapter 21Differential Meta-model and Particle Swarm Optimization
- Chapter 22Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem
- Chapter 23Finite Element Mesh Decomposition Using Evolving Ant Colony Optimization
- Chapter 24Swarm Intelligence and Image Segmentation
- Chapter 25Particle Swarm Optimization - Stochastic Trajectory Analysis and Parameter Selection
- Chapter 26Stochastic Metaheuristics as Sampling Techniques using Swarm Intelligence
- Chapter 27Artificial Ants in the Real World: Solving On-line Problems Using Ant Colony Optimization
- Chapter 28Preface: Swarm Intelligence, Focus on Ant and Particle Swarm Optimization
