A Novel Two Sub-swarms Exchange Particle Swarm Optimization Based on Multi-phases

Owing to the problem that particle swarm optimization algorithm is easily falling into local optima point in optimization of high-dimensional and complex functions. In this paper, a novel two sub-swarms exchange particle swarm optimization based on multi-phases(TSEM-PSO) is proposed. The particle swarm is divided into two identical sub-swarms, with the first adopting the standard PSO model, and the second adopting the proposed model, When the two sub-swarms evolve steady states independent, The exchange number of particle is different in different searching phase and its amount is gradually decreasing, which can increase the information exchange between the particles, improve the diversity of population and meliorate the convergence of algorithm. Experiment results show that the TSEM-PSO is superior to standard PSO and TSE-PSO algorithm.