Swarm Intelligence
Russell C. Eberhart · Yuhui Shi · James Kennedy
abr. del 2001 · Elsevier
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Traditional methods for creating intelligent computational systems haveprivileged private "internal" cognitive and computational processes. Incontrast, Swarm Intelligence argues that humanintelligence derives from the interactions of individuals in a social worldand further, that this model of intelligence can be effectively applied toartificially intelligent systems. The authors first present the foundations ofthis new approach through an extensive review of the critical literature insocial psychology, cognitive science, and evolutionary computation. Theythen show in detail how these theories and models apply to a newcomputational intelligence methodology—particle swarms—which focuseson adaptation as the key behavior of intelligent systems. Drilling downstill further, the authors describe the practical benefits of applying particleswarm optimization to a range of engineering problems. Developed bythe authors, this algorithm is an extension of cellular automata andprovides a powerful optimization, learning, and problem solving method. This important book presents valuable new insights by exploring theboundaries shared by cognitive science, social psychology, artificial life,artificial intelligence, and evolutionary computation and by applying theseinsights to the solving of difficult engineering problems. Researchers andgraduate students in any of these disciplines will find the materialintriguing, provocative, and revealing as will the curious and savvycomputing professional.* Places particle swarms within the larger context of intelligentadaptive behavior and evolutionary computation. * Describes recent results of experiments with the particle swarmoptimization (PSO) algorithm * Includes a basic overview of statistics to ensure readers canproperly analyze the results of their own experiments using thealgorithm. * Support software which can be downloaded from the publisherswebsite, includes a Java PSO applet, C and Visual Basic sourcecode.
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Sobre l'autor
Russ Eberhart is Associate Dean of Research at Purdue School of Engineering and Technology in Indianapolis, IN. He is the author of Neural Network PC Tools (Academic Press), a leading book in the field of Neural Networks. Among his credits, he is the former President of the IEEE Neural Networks Council.Yuhui Shi received the Ph.D. degree in electrical engineering from Southeast University, China, in 1992. Since then, he has worked at several universities including the Department of Radio Engineering, Southeast University, Nanjing, China, the Department of Electrical & Computer Engineering, Concordia University, Montreal, Canada, the Department of Computer Science, Australian Defense Force Academic, Canberra, Australia, the Department of Computer Science, Korean Advanced Institute of Science and Technology, Taejon, Korea, and the Department of Electrical Engineering, Purdue School of Engineering and Technology, Indianapolis, Indiana, USA. He is currently with Electronic Data Systems, Inc., Kokomo, Indiana, USA, as an Applied Specialist. His main interests include artificial neural networks, evolutionary computation, fuzzy logic systems and their industrial applications. Dr. Shi was a co-presenter of the tutorial, Introduction to Computation Intelligence, at the 1998 WCCI Conference, Anchorage, Alaska, and presented the tutorial, Evolutionary Computation and Fuzzy Systems, at the 1998 ANNIE Conference, St. Louis. He is the technical co-chair of 2001 Particle Swarm Optimization Workshop, Indianapolis, Indiana.James Kennedy is a social psychologist who works in survey methods at the US Department of Labor. He has conducted basic and applied research into social effects on cognition and attitude. Dr. Kennedy has worked with the particle swarm computer model of social influence in artificial communities since 1994, presenting research in both the computer-science and social-science publications.
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