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ANT COLONY OPTIMIZATION METHODS AND APPLICATIONS Edited by Avi Ostfeld Ant Colony Optimization - Methods and Applications Edited by Avi Ostfeld Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original 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. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Iva Lipovic Technical Editor Teodora Smiljanic Cover Designer Martina Sirotic Image Copyright kRie, 2010. Used under license from Shutterstock.com First published February, 2011 Printed in India A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org Ant Colony Optimization - Methods and Applications, Edited by Avi Ostfeld p. cm. ISBN 978-953-307-157-2 free online editions of InTech Books and Journals can be found at www.intechopen.com Contents Preface Part 1 Methods IX 1 Chapter 1 Multi-Colony Ant Algorithm Enxiu Chen and Xiyu Liu 3 Chapter 2 Continuous Dynamic Optimization 13 Walid Tfaili Chapter 3 An AND-OR Fuzzy Neural Network 25 Jianghua Sui Chapter 4 Some Issues of ACO Algorithm Convergence 39 Lorenzo Carvelli and Giovanni Sebastiani Chapter 5 On Ant Colony Optimization Algorithms for Multiobjective Problems 53 Jaqueline S. Angelo and Helio J.C. Barbosa Chapter 6 Automatic Construction of Programs Using Dynamic Ant Programming 75 Shinichi Shirakawa, Shintaro Ogino, and Tomoharu Nagao Chapter 7 A Hybrid ACO-GA on Sports Competition Scheduling 89 Huang Guangdong and Wang Qun Chapter 8 Adaptive Sensor-Network Topology Estimating Algorithm Based on the Ant Colony Optimization 101 Satoshi Kuriharam, Hiroshi Tamaki, Kenichi Fukui and Masayuki Numao Chapter 9 Ant Colony Optimization in Green Manufacturing Cong Lu 113 VI Contents Part 2 Applications 129 Chapter 10 Optimizing Laminated Composites Using Ant Colony Algorithms 131 Mahdi Abachizadeh and Masoud Tahani Chapter 11 Ant Colony Optimization for Water Resources Systems Analysis – Review and Challenges 147 Avi Ostfeld Chapter 12 Application of Continuous ACOR to Neural Network Training: Direction of Arrival Problem Hamed Movahedipour Chapter 13 Chapter 14 Ant Colony Optimization for Coherent Synthesis of Computer System Mieczysław Drabowski Ant Colony Optimization Approach for Optimizing Traffic Signal Timings Ozgur Baskan and Soner Haldenbilen 159 179 205 Chapter 15 Forest Transportation Planning Under Multiple Goals Using Ant Colony Optimization 221 Woodam Chung and Marco Contreras Chapter 16 Ant Colony System-based Applications to Electrical Distribution System Optimization Gianfranco Chicco 237 Chapter 17 Ant Colony Optimization for Image Segmentation 263 Yuanjing Feng and Zhejin Wang Chapter 18 SoC Test Applications Using ACO Meta-heuristic 287 Hong-Sik Kim, Jin-Ho An and Sungho Kang Chapter 19 Ant Colony Optimization for Multiobjective Buffers Sizing Problems 303 Hicham Chehade, Lionel Amodeo and Farouk Yalaoui Chapter 20 On the Use of ACO Algorithm for Electromagnetic Designs 317 Eva Rajo-Iglesias, Óscar Quevedo-Teruel and Luis Inclán-Sánchez Preface Invented by Marco Dorigo in 1992, Ant Colony Optimization (ACO) is a meta-heuristic stochastic combinatorial computational discipline inspired by the behavior of ant colonies which belong to a family of meta-heuristic stochastic methodologies such as simulated annealing, Tabu search and genetic algorithms. It is an iterative method in which populations of ants act as agents that construct bundles of candidate solutions, where the entire bundle construction process is probabilistically guided by heuristic imitation of ants’ behavior, tailor-made to the characteristics of a given problem. Since its invention ACO was successfully applied to a broad range of NP hard problems such as the traveling salesman problem (TSP) or the quadratic assignment problem (QAP), and is increasingly gaining interest for solving real life engineering and scientific problems. This book covers state of the art methods and applications of ant colony optimization algorithms. It incorporates twenty chapters divided into two parts: methods (nine chapters) and applications (eleven chapters). New methods, such as multi colony ant algorithms based upon a new pheromone arithmetic crossover and a repulsive operator, as well as a diversity of engineering and science applications from transportation, water resources, electrical and computer science disciplines are presented. The following is a list of the chapter’s titles and authors, and a brief description of their contents. Acknowledgements I wish to express my deep gratitude to all the contributing authors for taking the time and efforts to prepare their comprehensive chapters, and to acknowledge Ms. Iva Lipovic, InTech Publishing Process Manager, for her remarkable, kind and professional assistance throughout the entire preparation process of this book. Avi Ostfeld Haifa, Israel