By
Russell Eberhart, Purdue School of Engineering
Yuhui Shi, Electronic Data Systems, Inc.
James Kennedy, US Department of Labor
Description
Traditional methods for creating intelligent computational systems have
privileged private "internal" cognitive and computational
processes. In
contrast,
Swarm Intelligence argues that human
intelligence derives from the interactions of individuals in
a social world
and further, that this model of intelligence can be effectively applied to
artificially intelligent systems. The authors
first present the foundations of
this new approach through an extensive review of the critical literature in
social psychology, cognitive
science, and evolutionary computation. They
then show in detail how these theories and models apply to a new
computational intelligence
methodology—particle swarms—which focuses
on adaptation as the key behavior of intelligent systems. Drilling down
still further, the
authors describe the practical benefits of applying particle
swarm optimization to a range of engineering problems. Developed by
the
authors, this algorithm is an extension of cellular automata and
provides a powerful optimization, learning, and problem solving method.
This important book presents valuable new insights by exploring the
boundaries shared by cognitive science, social psychology,
artificial life,
artificial intelligence, and evolutionary computation and by applying these
insights to the solving of difficult engineering
problems. Researchers and
graduate students in any of these disciplines will find the material
intriguing, provocative, and revealing
as will the curious and savvy
computing professional.
