TABLE OF CONTENTS James Kennedy, U.S. Department of Labor, USA
Particle swarm optimization is a computer paradigm that is based on human social influence and cognition. Candidate problem solutions are randomly initialized, and improvements are found through interactions among them. Social-psychological aspects of the algorithm are described, followed by implementation details. The particle swarm operates in three kinds of spaces, namely a topological space comprising the social network structure of the population, a parameter space of problem variables, and a one-dimensional evaluative space. Variations in the algorithm are described, and finally it is compared to evolutionary computation models.
This chapter introduces the particle swarm algorithm, which is used to optimize hard problems. The algorithm is sometimes compared to evolutionary algorithms (EAs) of various sorts, as it comprises a population of individuals and random fluctuation, which are characteristic of EAs. The particle swarm arose from research in social psychology, and differs significantly from evolutionary methods. This chapter develops the algorithm from the sociocognitive perspective, describes some variations of the algorithm, and finally draws comparisons between particle swarms and related paradigms.
There is not only a close analogy between the operations of the mind in general reasoning and its operation in the particular science of Algebra, but there is to a considerable extent an exact agreement in the laws by which the two classes of operations are conducted. (Boole, 1854, p. 6)
George Boole s volume, An Investigation into the Laws of Thought, on Which are founded the Mathematical Theories of...
