A Study of Particle Swarm Optimization: Convergence Analysis and Parameter Selection
Particle Swarm Optimization (PSO) is a recently proposed algorithm by J. Kennedy and R. Eberhart in 1995, motivated by social behavior of organisms such as bird flocking and fish schooling. PSO algorithm is not only a tool for optimization, but also a tool for representing socio-cognition of human and artificial agents, based on principles of social behavior. Some scientists suggest that knowledge is optimized by social interaction and thinking is not only private but also interpersonal. PSO as an optimization tool, provides a population-based search procedure in which individuals called particles change their position (state) with time. In a PSO system, particles fly in a multidimensional search space. During flight, each particle adjusts its position according to its own experience, and according to the experience of K neighbors, making use of the best position encountered by itself and its neighbors. We propose in this paper, an extension of the PSO system that integrates a new displacement of the particles and we highlight a relation between the coefficients of update of each dimension velocity between the classical PSO algorithm and the extension. This extension was tested on five benchmarks continuous functions and on the RCPSP ,j30 instances provided by the PSPLIB. The results obtained are promising and encouraging.
Particle Swarm Optimization intensification diversification particle displacement
Michel GOURGAND Sylverin KEMMOE TCHOMTE Alain QUILLIOT
LIMOS UMR CNRS 6158,ISIMA,Universite Blaise Pascal,Campus des Cezeaux B.P 10125-63173 Aubiere Cedex, LIMOS UMR CNRS 6158,Compdexe scientifique des Cezeaux-63177 Aubiere Cedex,France
国际会议
北京
英文
2007-05-30（万方平台首次上网日期，不代表论文的发表时间）