THEORY AND NEW APPLICATIONS OF SWARM INTELLIGENCE Edited by Rafael Parpinelli and Heitor S. Lopes Theory and New Applications of Swarm Intelligence Edited by Rafael Parpinelli and Heitor S. Lopes Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2012 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. 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. 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Publishing Process Manager Romana Vukelic Technical Editor Teodora Smiljanic Cover Designer InTech Design Team First published March, 2012 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechopen.com Theory and New Applications of Swarm Intelligence, Edited by Rafael Parpinelli and Heitor S. Lopes p. cm. ISBN 978-953-51-0364-6 Contents Preface IX Chapter 1 Swarm-Based Metaheuristic Algorithms and No-Free-Lunch Theorems 1 Xin-She Yang Chapter 2 Analysis of the Performance of the Fish School Search Algorithm Running in Graphic Processing Units 17 Anthony J. C. C. Lins, Carmelo J. A. Bastos-Filho, Débora N. O. Nascimento, Marcos A. C. Oliveira Junior and Fernando B. de Lima-Neto Chapter 3 Social Emotional Optimization Algorithm with Random Emotional Selection Strategy 33 Zhihua Cui, Yuechun Xu and Jianchao Zeng Chapter 4 The Pursuit of Evolutionary Particle Swarm Optimization 51 Hong Zhang Chapter 5 Volitive Clan PSO - An Approach for Dynamic Optimization Combining Particle Swarm Optimization and Fish School Search 69 George M. Cavalcanti-Júnior, Carmelo J. A. Bastos-Filho and Fernando B. de Lima-Neto Chapter 6 Inverse Analysis in Civil Engineering: Applications to Identification of Parameters and Design of Structural Material Using Mono or Multi-Objective Particle Swarm Optimization 87 M. Fontan, A. Ndiaye, D. Breysse and P. Castéra Chapter 7 Firefly Meta-Heuristic Algorithm for Training the Radial Basis Function Network for Data Classification and Disease Diagnosis 115 Ming-Huwi Horng, Yun-Xiang Lee, Ming-Chi Lee and Ren-Jean Liou VI Contents Chapter 8 Under-Updated Particle Swarm Optimization for Small Feature Selection Subsets from Large-Scale Datasets 133 Victor Trevino and Emmanuel Martinez Chapter 9 Predicting Corporate Forward 2 Month Earnings 163 Michael F. Korns Preface Swarm Intelligence is a research field that studies the emergent collective intelligence of self-organized and decentralized simple agents. It is based on the social behavior that can be observed in nature, such as in flocks of birds, fish schools and bee hives, where a group of individuals with limited capabilities are able to emerge with intelligent solutions for complex problems. Since long ago, researchers in Computer Science have already realized the importance of emergent behaviors for complex problem solving. This book gathers together some recent advances on Swarm Intelligence, comprising new swarm-based optimization methods, hybrid algorithms and innovative applications. The contents of this book allows the reader to get acquainted with both theoretical and technical aspects and applications of Swarm Intelligence. I would like to thank the authors that made this book possible and Dr. Heitor Silvério Lopes for all the help during the review process. Dr. Rafael Stubs Parpinelli Universidade do Estado de Santa Catarina (UDESC) Brazil [...]... enhanced by the variations of loudness and 8 8 Theory and New Applications of Swarm Intelligence Will-be-set-by-IN-TECH pulse rates In this sense, the mechanism is relatively simple, but very efficient in balancing the two key components In the cuckoo search, things become more subtle Diversification is carried out in two ways: randomization via Lévy flights and feeding new solutions into randomly chosen nests... Problems, Proceedings of IPROMS 2006 Conference, pp.454-461 [30] Schumacher, C., Vose, M., and Whitley D., The no free lunch and problem description length, in: Genetic and Evolutionary Computation Conference, GECCO-2001, pp 565-570 (2001) 16 16 Theory and New Applications of Swarm Intelligence Will-be-set-by-IN-TECH [31] Sebastiani, G and Torrisi, G L., An extended ant colony algorithm and its convergence... Intensification is achieved by a combination of elitism and the generation of solutions according to similarity (thus the usage of local information) In addition, a switch parameter (a fraction of abandoned nests) is used to control the balance of diversification and intensification As seen earlier, an important component in swarm intelligence and modern metaheuristics is randomization, which enables an algorithm... due to the stochastic nature of these algorithms However, good results do exist, concerning randomization search techniques (Auger and Teytaud, 2010) Convergence analysis is another challenging area One of the main difficulties concerning the convergence analysis of metaheuristic algorithms is that no generic framework exists, though 2 2 Theory and New Applications of Swarm Intelligence Will-be-set-by-IN-TECH... B(t)) , (5) 20 4 Theory and New Applications of Swarm Intelligence Will-be-set-by-IN-TECH where r1 is a number randomly generated in the interval [0, 1] by an uniform probability density function d( xi (t), B(t)) evaluates the euclidean distance between the particle i and the barycenter stepvol is called volitive step and controls the step size of the fish stepvol is defined as a percentage of the search... results Therefore, it turns out that these GPUs are inappropriate to solve some types of problems The CUDA capacity to execute a high number of threads in parallel is due to the hierarchical organization of these threads as a grid of blocks A thread block is set of processes which 22 6 Theory and New Applications of Swarm Intelligence Will-be-set-by-IN-TECH cooperate in order to share data efficiently using... 2003; Neumann and Witt 2010; Parpinelli and Lopes, 2011) Good examples are particle swarm optimization (PSO) (Kennedy and Eberhart, 1995) and firefly algorithm (FA) (Yang, 2009) They work remarkably efficiently and have many advantages over traditional, deterministic methods and algorithms, and thus they have been applied in almost all area of science, engineering and industry (Floudas and Pardolos, 2009;... along flower patches and thus different search 4 4 Theory and New Applications of Swarm Intelligence Will-be-set-by-IN-TECH regions in the search space For a more comprehensive review, please refer to Parpinelli and Lopes (2011) Honeybees live in a colony and they forage and store honey in their constructed colony Honeybees can communicate by pheromone and ‘waggle dance’ For example, an alarming bee may... Gutjahr, W J., Convergence Analysis of Metaheuristics Annals of Information Systems, 10, 159-187 (2010) [16] He, J and Yu, X., Conditions for the convergence of evolutionary algorithms, J Systems Architecture, 47, 601-612 (2001) [17] Henderson, D., Jacobson, S H., and Johnson, W., The theory and practice of simulated annealing, in: Handbook of Metaheuristics (Eds F Glover and G A Kochenberger), Kluwer Academic,... 5 5 Swarm- Based Metaheuristic Algorithms and No-Free-Lunch Theorems Swarm- Based Metaheuristic Algorithms and No-Free-Lunch Theorems 2.3 Particle swarm optimization Particle swarm optimization (PSO) was developed by Kennedy and Eberhart in 1995, based on the swarm behaviour such as fish and bird schooling in nature Since then, PSO has generated much wider interests, and forms an exciting, ever-expanding . THEORY AND NEW APPLICATIONS OF SWARM INTELLIGENCE Edited by Rafael Parpinelli and Heitor S. Lopes Theory and New Applications of Swarm Intelligence Edited. either get rid of the egg, or simply abandon the nest and build a completely new nest. 6 Theory and New Applications of Swarm Intelligence Swarm- Based Metaheuristic Algorithms and No-Free-Lunch. continuous problems. 4 Theory and New Applications of Swarm Intelligence Swarm- Based Metaheuristic Algorithms and No-Free-Lunch Theorems 5 2.3 Particle swarm optimization Particle swarm optimization