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.
* Places particle swarms within the larger context of
intelligent
adaptive behavior and evolutionary computation.
* Describes recent results of experiments with the particle
swarm
optimization (PSO) algorithm
* Includes a basic overview of statistics to ensure readers
can
properly analyze the results of their own experiments using
the
algorithm.
* Support software which can be downloaded from the
publishers
website, includes a Java PSO applet, C and Visual Basic
source
code."
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