- Russell Eberhart, Purdue School of Engineering
- Yuhui Shi, Electronic Data Systems, Inc.
- James Kennedy, US Department of Labor
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 methodologyparticle swarmswhich 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.
Hardbound, 512 Pages
Published: March 2001
Imprint: Morgan Kaufmann
Well received the September UK Game industry show. Recent publicity includes a mention in Visual Basic Design Magazine, June issue.
- Introduction Part 1: Foundations Life and Intelligence Optimization by Trial and Error On our Nonexistence as Entities Evolutionary Computation Theory and Paradigms Humans - Actual, Imagined and Implied Thinking is Social Part 2: Particle Optimization and Collective Intelligence The Binary Particle Swarm Variations and Comparisons; Applications Implications and Speculations Conclusions