Swarm Intelligence (The Morgan Kaufmann Series in Evolutionary Computation)

Swarm Intelligence (The Morgan Kaufmann Series in Evolutionary Computation)

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<p>Traditional methods for creating intelligent computational systems have<br>privileged private "internal" cognitive and computational processes. In<br>contrast, <i>Swarm Intelligence</i> argues that human<br>intelligence derives from the interactions of individuals in a social world<br>and further, that this model of intelligence can be effectively applied to<br>artificially intelligent systems. The authors first present the foundations of<br>this new approach through an extensive review of the critical literature in<br>social psychology, cognitive science, and evolutionary computation. They<br>then show in detail how these theories and models apply to a new<br>computational intelligence methodology―particle swarms―which focuses<br>on adaptation as the key behavior of intelligent systems. Drilling down<br>still further, the authors describe the practical benefits of applying particle<br>swarm optimization to a range of engineering problems. Developed by<br>the authors, this algorithm is an extension of cellular automata and<br>provides a powerful optimization, learning, and problem solving method. <br><p><br>This important book presents valuable new insights by exploring the<br>boundaries shared by cognitive science, social psychology, artificial life,<br>artificial intelligence, and evolutionary computation and by applying these<br>insights to the solving of difficult engineering problems. Researchers and<br>graduate students in any of these disciplines will find the material<br>intriguing, provocative, and revealing as will the curious and savvy<br>computing professional. <br><br>* Places particle swarms within the larger context of intelligent<br>adaptive behavior and evolutionary computation. <br>* Describes recent results of experiments with the particle swarm<br>optimization (PSO) algorithm <br>* Includes a basic overview of statistics to ensure readers can<br>properly analyze the results of their own experiments using the<br>algorithm. <br>* Support software which can be downloaded from the publishers<br>website, includes a Java PSO applet, C and Visual Basic source<br>code.