ANT COLONY OPTIMIZATION METHODS AND APPLICATIONS pot

ANT COLONY OPTIMIZATION  METHODS AND APPLICATIONS Edited by Avi Os eld 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 Part 1 Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Preface IX Methods 1 Multi-Colony Ant Algorithm 3 Enxiu Chen and Xiyu Liu Continuous Dynamic Optimization 13 Walid Tfaili An AND-OR Fuzzy Neural Network 25 Jianghua Sui Some Issues of ACO Algorithm Convergence 39 Lorenzo Carvelli and Giovanni Sebastiani On Ant Colony Optimization Algorithms for Multiobjective Problems 53 Jaqueline S. Angelo and Helio J.C. Barbosa Automatic Construction of Programs Using Dynamic Ant Programming 75 Shinichi Shirakawa, Shintaro Ogino, and Tomoharu Nagao A Hybrid ACO-GA on Sports Competition Scheduling 89 Huang Guangdong and Wang Qun Adaptive Sensor-Network Topology Estimating Algorithm Based on the Ant Colony Optimization 101 Satoshi Kuriharam, Hiroshi Tamaki, Kenichi Fukui and Masayuki Numao Ant Colony Optimization in Green Manufacturing 113 Cong Lu Contents Contents VI Applications 129 Optimizing Laminated Composites Using Ant Colony Algorithms 131 Mahdi Abachizadeh and Masoud Tahani Ant Colony Optimization for Water Resources Systems Analysis – Review and Challenges 147 Avi Ostfeld Application of Continuous ACOR to Neural Network Training: Direction of Arrival Problem 159 Hamed Movahedipour Ant Colony Optimization for Coherent Synthesis of Computer System 179 Mieczysław Drabowski Ant Colony Optimization Approach for Optimizing Traffic Signal Timings 205 Ozgur Baskan and Soner Haldenbilen Forest Transportation Planning Under Multiple Goals Using Ant Colony Optimization 221 Woodam Chung and Marco Contreras Ant Colony System-based Applications to Electrical Distribution System Optimization 237 Gianfranco Chicco Ant Colony Optimization for Image Segmentation 263 Yuanjing Feng and Zhejin Wang SoC Test Applications Using ACO Meta-heuristic 287 Hong-Sik Kim, Jin-Ho An and Sungho Kang Ant Colony Optimization for Multiobjective Buffers Sizing Problems 303 Hicham Chehade, Lionel Amodeo and Farouk Yalaoui On the Use of ACO Algorithm for Electromagnetic Designs 317 Eva Rajo-Iglesias, Óscar Quevedo-Teruel and Luis Inclán-Sánchez Part 2 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter 18 Chapter 19 Chapter 20 Pref ac e Invented by Marco Dorigo in 1992, Ant Colony Optimization (ACO) is a meta-heuris- tic 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 scien- tifi c problems. This book covers state of the art methods and applications of ant colony optimiza- tion 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 opera- tor, as well as a diversity of engineering and science applications from transportation, water resources, electrical and computer science disciplines are presented. The follow- ing 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 eff orts to prepare their comprehensive chapters, and to acknowledge Ms. Iva Li- povic, InTech Publishing Process Manager, for her remarkable, kind and professional assistance throughout the entire preparation process of this book. Avi Ostfeld Haifa, Israel [...]... using both a pheromone crossover and a repulsive operator based on multi-optimum for the worst ant colony; 8 6 Ant Colony Optimization - Methods and Applications Chooses one of the worst ant colonies to kill and sends the new colony parameters and kill command to the slave processor who owns the killed colony; 7 Checks convergence • Slave processor 1 Receives a set of colony s parameters from the master... concepts and rules of this multi -colony ant system This paper is organized as follows Section II briefly explains the basic ACO algorithm and its main variant MMAS we use as a basis for multi -colony ant algorithm In Section III we 4 Ant Colony Optimization - Methods and Applications describe detailed how to use both the pheromone crossover and the repulsive operator to reinitialize a stagnated colony. .. Methods 1 Multi -Colony Ant Algorithm 1School Enxiu Chen1 and Xiyu Liu2 of Business Administration, Shandong Institute of Commerce and Technology, Jinan, Shandong; 2School of Management & Economics, Shandong Normal University, Jinan, Shandong, China 1 Introduction The first ant colony optimization (ACO) called ant system was inspired through studying of the behavior of ants in 1991 by Macro Dorigo and. .. illustrating the capability of ant colonies of finding the shortest path, in the case there are only two paths of different lengths between the nest and the food source 2 14 Ant Colony Optimization Ant Colony Optimization - Methods and Applications Some ant colony techniques aimed at dynamic optimization have been described in the literature In particular, Johann Dr´ o and Patrick Siarry introduced... of the ants is influenced by the characteristics of the pheromone matrix, which explains the complex dynamic behavior Various tests were carried 4 16 Ant Colony Optimization Ant Colony Optimization - Methods and Applications out on a permutation problem But the authors did not deal with really dynamic problems In section 5 we will present a new ant colony algorithm aimed at dynamic continuous optimization. .. probability and repeating the process until the components number is equal to N In discrete problems, solutions are not known in advance, which means that pheromones cannot be attributed to a complete solution, but to solution components 6 18 Ant Colony Optimization Ant Colony Optimization - Methods and Applications Fig 4 Every ant constructs a complete solution which includes n dimensions 5 CANDO: charged ants... Optimization , Artificial Life and Robotics, 7(4), pp 198-204, 2004 12 Ant Colony Optimization - Methods and Applications [8] D Robilliard and C Fonlupt, “A Shepherd and a Sheepdog to Guide Evolutionary Computation”, Artificial Evolution, pp 277-291, 1999 [9] B Koh, A George, R Haftka, and B Fregly , “Parallel Asynchronous Particle Swarm Optimization , International Journal for Numerical Methods in Engineering,... kill command and a set of new parameters to it Report Results • Slave processor Receive Initialize parameters from the master processor Initialize a new local ant colony Perform Optimization For k = 1, number of iterations For i = 1, number of ants Construct a new solution 9 Multi -Colony Ant Algorithm If (kill command and a set of new parameters received) then Goto initialize a new local ant colony; ... solution Update the weight (pheromone) based on the best found solution End Choose the best solution among the m ants Choose the best solution among current and old solutions End while 8 20 Ant Colony Optimization Ant Colony Optimization - Methods and Applications 6 Experimental results CANDO 4.27(4.02) 11.54(5.2) 0.29(4.07) 1.78(8.09) 12.3(0.37) 4.77E-005(0) 2.87(2.24) AbPoP AbVP APhL OVP OPoL AVP... with gene dependent mutation probability for non-stationary optimization problems, Congress on Evolutionary Computation, Vol 2 Trojanowski, K & Michalewicz, Z (2000) Evolutionary Optimization in Non-Stationary Environments, Journal of Computer Science and Technology 1(2): 93–124 12 24 Ant Colony Optimization Ant Colony Optimization - Methods and Applications URL: http://journal.info.unlp.edu.ar/journal/journal2/papers/Mica.zip . ANT COLONY OPTIMIZATION  METHODS AND APPLICATIONS Edited by Avi Os eld Ant Colony Optimization - Methods and Applications Edited by Avi Ostfeld Published. Using Ant Colony Optimization 221 Woodam Chung and Marco Contreras Ant Colony System-based Applications to Electrical Distribution System Optimization 237 Gianfranco Chicco Ant Colony Optimization. crossover and a repulsive operator based on multi-optimum for the worst ant colony; Ant Colony Optimization - Methods and Applications 8 6. Chooses one of the worst ant colonies to kill and