Swarm Intelligence

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.