Chaos embedded particle swarm optimization algorithms

Abstract

This paper proposes new particle swarm optimization (PSO) methods that use chaotic maps for parameter adaptation. This has been done by using of chaotic number generators each time a random number is needed by the classical PSO algorithm. Twelve chaos-embedded PSO methods have been proposed and eight chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of PSO and complex dynamics, call improve the quality of results in sonic optimization problems. It has been also shown that, sonic of the proposed methods have somewhat increased the solution quality, that is in sonic cases they improved the global searching capability by escaping the local solutions. (C) 2007 Elsevier Ltd. All rights reserved.