多种群协同粒子群算法在函数最优问题中的应用

Particle swarm optimization(PSO) algorithm has been developing rapidly and has been applied wiedly for its simplicity and relatively fast convergence speed.But the standard PSO obtain local optimum frequently,which limited the scope of application of the algorithm.This paper presents a Multi-Population Cooperative Particle Swarm Optimizer,which inspirated form communication of the populations of the animals.The algorithm is based on a master-slave model,in which a population consists of a master group and several slave groups.The slave groups execute a single PSO independently to maintain the diversity of particles,while the master group evolves based on its own information and also the information of the slave groups.The master group and the slave groups communication through employing a parameter.The slave groups encourage the global exploration,while the master group promotes local exploitation.The relationship between the master group and slave groups can keep of a right balance of exploration and exploitation,which is essential for the success of a given optimization task.The effectiveness of the algorithm is proved by experiments.