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Particle Swarm Optimization: Theory, Techniques and Applications
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Editors: Andrea E. Olsson
Book Description:
Particle swarm optimization (PSO) is an algorithm modeled on swarm intelligence that finds a solution to an optimization problem in a search space or model and predicts social behavior in the presence of objectives. The PSO is a stochastic, population-based computer algorithm modeled on swarm intelligence. Swarm intelligence is based on social-psychological principles and provides insights into social behavior, as well as contributing to engineering applications. This book presents information on particle swarm optimization such as using mono-objective and multi-objective particle swarm optimization for the tuning of process control laws; convergence issues in particle swarm optimization and vehicle routing problems using enhanced particle swarm optimization.

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Table of Contents:
Preface

Using Mono-Objective and Multi-Objective Particle Swarm Optimization for the Tuning of Process Control Laws, pp. 1-30
(Guillaume Sandou, Saïd Ighobriouen, SUPELEC, Automatic Control Department, Gif-sur-Yvette, France)

Study on Vehicle Routing Problem with Time Windows Based on Enhanced Particle Swarm Optimization Approach, pp. 31-48
(Chun-Ta Lin, Department of Information Management, Yu-Da University,
Miao-li, Taiwan, R.O.C)

Reliability Optimization Problems using Adaptive Genetic Algorithm and Improved Particle Swarm Optimization, pp. 49-66
(YoungSu Yun, Division of Business Administration, Chosun University, Dong-gu, Gwangju, Republic of Korea)

Convergence Issues in Particle Swarm Optimization , pp. 67-95
(David Bradford Jr., Chih-Cheng Hung, Southern Polytechnic State University, Marietta, Georgia, USA)

Globally Convergent Modifications of Particle Swarm Optimization for Unconstrained Optimization, pp. 97-118
(Emilio F. Campana, Giovanni Fasano, Daniele Peri, Istituto Nazionale per Studi ed Esperienze di Architettura Navale (INSEAN), Rome, Italy, and others)

Nonlinear 0-1 Programming through Particle Swarm Optimization using Decoding Algorithms, pp. 119-126
(Takeshi Matsui, Masatoshi Sakawa, Graduate School of Engineering, Hiroshima University, Hiroshima, Japan)

Comparative Study of Different Approaches to Particle Swarm Optimization in Theory and Practice, pp. 127-167
(Stefanie Thiem, Jorg Lassig, Department of Mathematics and Statistics, The Open University, Milton Keynes, United Kingdom, and others)

Particle Swarm Optimization for Computer Graphics and Geometric Modeling: Recent Trends, pp. 169-192
(Akemi Galvez, Andres Iglesias, Department of Applied Mathematics and Computational Sciences, University of Cantabria, Santander, Spain)

Particle Swarm Optimization Used for Mechanism Design and Guidance of Swarm Mobile Robots ^, pp. 193-225
(Peter Eberhard and Qirong Tang, Institute of Engineering and Computational Mechanics, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany)

A New Neighborhood Topology for the Particle Swarm Optimization Algorithm, pp. 227-247
(Angel Eduardo Munoz-Zavala, Arturo Hernandez-Aguirre, Enrique Raul Villa-Diharce, Universidad Autonoma de Aguascalientes, Agucalientes, AGS, Mexico, and others)

PSO Assisted Multiuse Detection for DS-CDMA Communication Systems, pp. 249-298
(Taufik Abrao, Leonardo Dagui de Oliveira, Bruno Augusto Angelico, Paul Jean Etienne Jeszensky, DEEL-UEL, Electrical Eng. Dept., State University of Londrina, PR, Brazil, and others)

Index

   Series:
      Engineering Tools, Techniques and Tables
   Binding: Hardcover
   Pub. Date: 2011
   Pages: 7 x 10, 305 pp.
   ISBN: 978-1-61668-527-0
   Status: AV
  
Status Code Description
AN Announcing
FM Formatting
PP Page Proofs
FP Final Production
EP Editorial Production
PR At Prepress
AP At Press
AV Available
  
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Particle Swarm Optimization: Theory, Techniques and Applications