The Improvement of Particle Swarm Optimization with Social Stereotyping Ideology

摘要

The Particle Swarm Optimization (PSO) has been wide used in filed of logistics. Theimprovement of PSO with Social Stereotyping Ideology is researched in this paper.The particle is updatednot only by the best previous position and the best position among all the particles in the swarm, but alsoby the best previous position of the most particles in common tend to.Aim to balance these merits anddemerits between hunting in local and exploitation in whole,and convergence to the global optimumsolution,a linearly varying acceleration coefficient over the generations is introduced.The results showthat the algorithm has better probability of finding global optimum and mean best value than othcrsalgorithm and better enhance the ability of hunt best.