Tutorial one on PSO
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Introduction to PSO
Russ Eberhart and James Kennedy are the originators of the particle swarm algorithm, and speak with more than a decade's experience in the field. This tutorial will begin with the basics, including not only the formulas but the philosophy of particle swarming. Engineering applications will be discussed, as well as an overview of some of the issues that have arisen and modifications to the algorithm that have been developed. Finally, the tutorial will present some new ideas, including new standards, new methods, and new ideas. The tutorial should be of interest to those who are new to the topic as well as researchers who know the field.
Bios
Russell C. Eberhart is Professor of Electrical and Computer Engineering at the Purdue School of Engineering and Technology, Indiana University Purdue University Indianapolis (IUPUI). He is also Vice President of Computelligence LLC, Indianapolis, Indiana. He received his Ph.D. from Kansas State University in electrical engineering. He is co-editor of a book on neural networks, and co-author of Computational Intelligence PC Tools, published in 1996 by Academic Press. He is co-author of a book with Jim Kennedy and Yuhui Shi entitled Swarm Intelligence, published by Morgan Kaufmann/Academic Press in April 2001. He was awarded the IEEE Third Millenium Medal. In 2001, he became a Fellow of the IEEE, and in 2002 he became a Fellow of the American Institute for Medical and Biological Engineering. He is the co-author, with Yuhui Shi, of a book entitled Computational Intelligence: Concepts to Implementations, to be published by Morgan Kaufmann/Elsevier.
James Kennedy is a social psychologist who has been working with the particle swarm algorithm since 1994. He received his Ph.D. in 1992 from the University of North Carolina, and works for the US Department of Labor in Washington, DC. He has published dozens of articles and chapters on particle swarms and related topics, in both computer-science and social-science journals and Proceedings. The Morgan Kaufmann volume, "Swarm Intelligence," by Kennedy and Russell C. Eberhart with Yuhui Shi, is now in its third printing.
Tutorial two on PSO
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Recent Advances in Particle Swarm Optimization
Dr. P. N. Suganthan
Nanyang Technological University
Singapore
Abstract
Particle Swarm Optimizer (PSO) is an efficient evolutionary algorithm and has developed rapidly in recent years. PSO is based on swarm intelligence. It is somewhat different from other evolutionary algorithm as each particle in the swarm has a velocity and a pbest and a gbest position to record and exploit its historical best position. In addition, global best position, gbest is also remembered and exploited. Since Kennedy and Eberhart introduced it in 1995, it has attracted much attention and many research groups are actively working on it. Many interesting and improved variants of PSO have been developed on Kennedy and Eberhart's work and it has found numerous applications in many areas. In this tutorial, we will review the recent developments in PSO, present some important variants proposed by different researchers. In addition, the parameter adaptation/tuning in PSO, multi-objective PSO algorithms, constraint PSO will also be discussed. The tutorial will also briefly present our recent efforts to develop novel benchmark test functions to evaluate numerical function optimization algorithms. The
problems of the existing test functions are discussed and novel single-objective, multi-objective and constrained test functions are introduced based on the analysis. This tutorial is timely and significant, as it provides a comprehensive overview of an emerging and important field.
Dr. P. N. Suganthan received the B.A degree, Postgraduate Certificate and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK. He obtained his Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. He was a predoctoral Research Assistant in the Department of Electrical Engineering, University of Sydney in 1995-96 and a lecturer in the Department of Computer Science and Electrical Engineering, University of Queensland in 1996-99. Since 1999 he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore where he was an Assistant Professor and now is an Associate Professor. He is an associate editor of the IEEE Transactions on Evolutionary Computation, Pattern Recognition Journal and International Journal of Computational Intelligence. He has been jointly organizing special sessions at Congress on Evolutionary Computation (CEC-2005, CEC-06, CEC-07) on benchmarking evolutionary algorithms. These benchmark problems have been well received by the evolutionary computation community. His research interests include evolutionary computation, pattern recognition, bioinformatics, biometrics, multiobjective evolutionary algorithms, applications of evolutionary computation and neural networks. He is a senior member of the IEEE.
Tutorial three on cultural algorithm
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CULTURAL ALGORITHMS: HARNESSING THE POWER OF SOCIAL INTELLIGENCE
Dr. Robert G. Reynolds
Professor
Computer Science
424 State Hall
Wayne State University
Detroit, Michigan 48124
ABSTRACT:
Cultural Algorithms were developed by Reynolds as a computational framework in which to embed social learning in an evolutionary context[1979]. A Cultural Algorithm consists of a Cultural Belief Space, a Population Space, and an interaction protocol that links the two together. In this tutorial we propose to discuss the following:
1. What they are:
A basic description of the Cultural Algorithm Framework.
2. Why they work:
Here we discuss the basic phases of the problem solving process in Cultural Algorithms and how those phases emerge from the interaction of the knowledge sources in the belief space, knowledge swarms, and the population of problem solvers in the population space.
3. How they work:
Here we focus on how the interaction of the knowledge sources at the meta-level, knowledge swarms, in the Belief Space produces social swarms in the population space. An implicit search
algorithm emerges from the interaction of these knowledge sources and its properties are discussed.
4. When will they work?
Since Cultural Algorithms derive their power from the emergence of knowledge and population swarms, what problems are suitable for solution with Cultural Algorithms and what problems will be hard or deceptive?
5. How to Develop Example Applications Using Simple Cultural Algorithms Shell.
6. Large Scale Application Examples
Optimization of Engineering Designs
Optimization in Dynamic Environments
Industrial Applications:
Emergence of Cultural Systems
Emergence of Urban Centers
Dr. Robert G. Reynolds received his Ph.D. degree in Computer Science, specializing in Artificial Intelligence, in 1979 from the University of Michigan, Ann Arbor. He is currently a professor of computer science and director of the Artificial Intelligence Laboratory at Wayne State University. He is also an Adjunct Associate Research Scientist with the Museum of Anthropology at the University of Michigan-Ann Arbor, and with the Complex Systems Group at the University of Michigan-Ann Arbor. His interests are in the development of computational models of cultural evolution for use in the simulation of complex organizations. Dr. Reynolds produced a framework, Cultural Algorithms, in which to express and computationally test various theories of social evolution using multi-agent simulation models. He has applied these techniques to problems concerning the origins of the state in the Valley of Oaxaca, Mexico (with Kent Flannery), and the origins of language and culture (with Robert Whallon), and the disappearance of the Ancient Anazazi (with Tim Kohler). He has co-authored two books, Flocks of the Wamani (1989, Academic Press), with Joyce Marcus and Kent V. Flannery; and The Acquisition of Software Engineering Knowledge (2003, Academic Press), with George Cowan. He is also co-editor of four books on evolutionary computation.
He has received funding from both government (NSF) and industry to support his work. He has published papers on the evolution of social intelligence in Scientific American, IEEE Transactions of Evolutionary Computation, IEEE Transactions on Systems, Man, and Cybernetics, IEEE Software, Communications of the ACM, and the Proceedings of the National Academy of Sciences among others.
He is currently an associate editor for the IEEE Transactions on Evolutionary Computation, the International Journal of Artificial Intelligence Tools, International Journal of Computational and Mathematical Organization Theory, and the International Journal of Software Engineering and Knowledge Engineering. He is also on the Advisory Board for the International Swarm Intelligence Symposium (2007, tutorials chair for the 2006 IEEE Swarm Intelligence Symposium, the treasurer for the NAACSOS association (National Association of Agent-Based modeling), and President of the Evolutionary Programming Society.
Tutorial Four on Ant Colony Optimization
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This tutorial starts with a presentation of biological information on ants. After an introduction of the basic ideas of Ant Colony Optimization (ACO) we discuss several aspects that are important for the design of an ACO algorithm in more detail. In particular, the optimization behaviour of ACO algorithms is analyzed and the right use of the pheromone information discussed. In the second part of the tutorial we give a personal view on specific aspects of ACO. Examples are the modelling of ACO algorithms, Multi Objective Optimization with ACO, parallel ACO algorithms, and ACO for dynamic and stochastic problems.
Bio:
Martin Middendorf received the Diploma degree in Mathematics and a Dr. rer. nat. at the University of Hannover, Germany. He gained his professoral Habilitation in 1998 at the University of Karlsruhe, Germany. He has worked at the Universities of Dortmund, Eichstätt, and Hannover, all Germany, as a visiting or permanent Professor of Computer Science. Currently he is professor for Parallel Computing and Complex Systems with the University of Leipzig, Germany. His research interests include swarm intelligence, algorithms from nature, bioinformatics, reconfigurable architectures and Organic Computing. He has published papers on ACO and swarm intelligence since several years and has given PhD courses on ACO and Swarm Intelligence at universities in different countries.
