Particle Swarm Optimization - A Survey

Keisuke KAMEYAMA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E92-D   No.7   pp.1354-1361
Publication Date: 2009/07/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.1354
Print ISSN: 0916-8532
Type of Manuscript: INVITED PAPER (Special Section on Large Scale Algorithms for Learning and Optimization)
Category: 
Keyword: 
particle swarm optimization,  swarm intelligence,  

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Summary: 
Particle Swarm Optimization (PSO) is a search method which utilizes a set of agents that move through the search space to find the global minimum of an objective function. The trajectory of each particle is determined by a simple rule incorporating the current particle velocity and exploration histories of the particle and its neighbors. Since its introduction by Kennedy and Eberhart in 1995, PSO has attracted many researchers due to its search efficiency even for a high dimensional objective function with multiple local optima. The dynamics of PSO search has been investigated and numerous variants for improvements have been proposed. This paper reviews the progress of PSO research so far, and the recent achievements for application to large-scale optimization problems.