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The particel swarm: Explosion, stability, and convergence in a multi-dimensional complex space
| Venue: | IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTION |
| Citations: | 812 - 10 self |
BibTeX
@ARTICLE{Clerc_theparticel,
author = {Maurice Clerc and James Kennedy},
title = {The particel swarm: Explosion, stability, and convergence in a multi-dimensional complex space },
journal = {IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTION},
year = {},
pages = {58--73}
}
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Abstract
The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained how it works. Further, traditional versions of the algorithm have had some dynamical properties that were not considered to be desirable, notably the particles’ velocities needed to be limited in order to control their trajectories. The present paper analyzes the particle’s trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A 5-dimensional depiction is developed, which completely describes the system. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system’s convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the optimization power of the particle swarm
Keyphrases
particel swarm multi-dimensional complex space particle swarm original algorithm discrete time complex search space generalized model dynamical property particle trajectory 5-dimensional depiction continuous time optimal region system convergence tendency analytical view particle swarm optimizer optimization power present paper traditional version algebraic view particle velocity social interaction
