Fuzzy logic based multi-optimum programming in particle swarm optimization

It is effective to avoid falling into local optimums at the original stage of the computation that the knowledge of multi-optimum distribution state is introduced into general programming of the particle swarm movement in Particle swarm optimization algorithm [18]. But if the proportion factor of multi-optimum programming can not be dynamic adjusted in the optimization process, the performance of the algorithm will be limited. In this paper, fuzzy logic was introduced into the process of multi-optimum dynamic programming, and a kind of particle swarm algorithm based on fuzzy logic and multi-optimum programming was put forward and simulated. Simulation results show that, the general convergence character of the algorithm derived in this paper has better performance than traditional PSO, fuzzy adaptive PSO[171 and static multi-optimum programming PSO algorithm [18] proposed by authors previously.