A modified adaptive particle swarm optimization algorithm

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. But if the proportion factor of multi-optimum programming cannot be dynamic adjusted in the optimization process, the performance of the algorithm will be limited. In this paper, a modified adaptive particle swarm optimization algorithm based on fuzzy and adaptive programming of multi-optimum was put forward and simulated. In the modified algorithm derived in this paper, proportion factor of multi-optimum programming can be dynamic adjusted in the optimization process, and simulation results show that it has well general convergence character.