Particle-swarm-optimization.pdf-资料库

Particle Swarm Optimization

Particle Swarm Optimization Edited by Aleksandar Lazinica In-Tech intechweb.org

IV Published by In-Tech In-Tech Kirchengasse 43/3, A-1070 Vienna, Austria Hosti 80b, 51000 Rijeka, Croatia Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2009 In-tech www.intechweb.org Additional copies can be obtained from: publication@intechweb.org First published January 2009 Printed in Croatia Particle Swarm Optimization, Edited by Aleksandar Lazinica ISBN 978-953-7619-48-0 1. Particle Swarm Optimization I. Aleksandar Lazinica p. cm.

V Preface Particle swarm optimization (PSO) is a population based stochastic optimization tech- nique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. This book is certainly a small sample of the research activity on Particle Swarm Optimiza- tion going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects. Special thanks to all authors, which have invested a great deal of time to write such inter- esting and high quality chapters. Aleksandar Lazinica

2. 3. 4. 5. 6. 7. 8. 9. VII Contents V 001 011 049 077 089 113 131 145 155 169 183 Preface 1. Novel Binary Particle Swarm Optimization Mojtaba Ahmadieh Khanesar, Hassan Tavakoli, Mohammad Teshnehlab and Mahdi Aliyari Shoorehdeli Swarm Intelligence Applications in Electric Machines Amr M. Amin and Omar T. Hegazy Particle Swarm Optimization for HW/SW Partitioning M. B. Abdelhalim and S. E. –D Habib Particle Swarms in Statistical Physics Andrei Bautu and Elena Bautu Individual Parameter Selection Strategy for Particle Swarm Optimization Xingjuan Cai, Zhihua Cui, Jianchao Zeng and Ying Tan Personal Best Oriented Particle Swarm Optimizer Chang-Huang Chen, Jong-Chin Hwang and Sheng-Nian Yeh Particle Swarm Optimization for Power Dispatch with Pumped Hydro Po-Hung Chen Searching for the best Points of interpolation using swarm intelligence techniques Djerou L., Khelil N., Zerarka A. and Batouche M. 10. A Particle Swarm Optimization technique used for the improvement of Particle Swarm Optimization and Other Metaheuristic Methods in Hybrid Flow Shop Scheduling Problem M. Fikret Ercan analogue circuit performances Mourad Fakhfakh, Yann Cooren, Mourad Loulou and Patrick Siarry 11. Particle Swarm Optimization Applied for Locating an Intruder by an Ultra-Wideband Radar Network Rodrigo M. S. de Oliveira, Carlos L. S. S. Sobrinho, Josivaldo S. Araújo and Rubem Farias

VIII 12. Application of Particle Swarm Optimization in Accurate Segmentation of Brain MR Images Nosratallah Forghani, Mohamad Forouzanfar , Armin Eftekhari and Shahab Mohammad-Moradi 13. Swarm Intelligence in Portfolio Selection 14. Enhanced Particle Swarm Optimization for Design and Optimization of Shahab Mohammad-Moradi, Hamid Khaloozadeh, Mohamad Forouzanfar, Ramezan Paravi Torghabeh and Nosratallah Forghani Frequency Selective Surfaces and Artificial Magnetic Conductors Simone Genovesi, Agostino Monorchio and Raj Mittra 15. Search Performance Improvement for PSO in High Dimensional Sapece 16. Finding Base-Station Locations in Two-Tiered Wireless Sensor Networks Toshiharu Hatanaka, Takeshi Korenaga, Nobuhiko Kondo and Katsuji Uosaki by Particle Swarm Optimization Tzung-Pei Hong, Guo-Neng Shiu and Yeong-Chyi Lee 17. Particle Swarm Optimization Algorithm for Transportation Problems 18. A Particle Swarm Optimisation Approach to Graph Permutations Han Huang and Zhifeng Hao Omar Ilaya and Cees Bil 19. Particle Swarm Optimization Applied to Parameters Learning of Probabilistic Neural Networks for Classification of Economic Activities Patrick Marques Ciarelli, Renato A. Krohling and Elias Oliveira 20. Path Planning for Formations of Mobile Robots using PSO Technique Martin Macaš, Martin Saska, Lenka Lhotská, Libor Přeučil and Klaus Schilling 21. Simultaneous Perturbation Particle Swarm Optimization 22. Particle Swarm Optimization with External Archives for Interactive Fuzzy and Its FPGA Implementation Yutaka Maeda and Naoto Matsushita Multiobjective Nonlinear Programming Takeshi Matsui, Masatoshi Sakawa, Kosuke Kato and Koichi Tamada 23. Using Opposition-based Learning with Particle Swarm Optimization and Barebones Differential Evolution Mahamed G.H. Omran 24. Particle Swarm Optimization: Dynamical Analysis through Fractional Calculus E. J. Solteiro Pires, J. A. Tenreiro Machado and P. B. de Moura Oliveira 203 223 233 249 261 275 291 313 329 347 363 373 385

资料库

Particle-swarm-optimization.pdf

相关推荐

人工智能

热门标签

最新资料