The Improvement Algorithm Basied Stochastic Particle Swarm Optimization

Particle Swarm Optimization(PSO) is a random optimization algorithm .The basic idea of PSO comes from the research of the behavior of birds find habitat. Since the algorithm has been proposed, because of its easy implementation, fast convergence, and the need to adjust less parameters, attracted wide attention from many scholars. The following two problems have been chosen as the main goal in the dissertation.(1) Construct a few algorithms based on SPSO and cooperative evolutionary.(2) Develop a mixed search method by combining SPSO with other optimization algorithms.The content of this paper is organized into four chapters.In chapter I, we introduce the history and status about particle swarm optimization, the creativities and the practical importance of this paper.In chapter II, A modified cooperative stochastic particle swarm optimization(CSPSO), is presented based on the analysis of the SPSO and the cooperative evolutionary PSO with multi-populations. The whole group is divided into several sub-groups. Every subgroup evolved independently and updated sharing information periodically.In chapter III, The stochastic particle swarm optimization basied the gradient method, is presented based on the analysis of the SPSO and the gradient of the continuous-differential function.Numerical experiments have proved that this hybrid algorithm is very reasonable.In chapter IV, A cooperative Simplex Method-Stochastic Particle Swarm Optimization(SM-SPSO) is proposed, The conception of multipopulations is adopted in this method,where SPSO and SM run on odd populations and even populations,respectively. Experimental results on optimization two benchmark functions demonstrate its usefulness.