An improved Particle Swarm Optimization algorithm

摘要

An improved Particle Swarm Optimization (IPSO) algorithm is proposed in this paper. In the algorithm, a premature estimate mechanism is introduced to judge whether the particles accumulate in a small region and tell the probability whether the swarm is trapped in a local optimum. If the estimate criterion is satisfied, the chaotic mutation operation, which makes use of the chaos search strategy and the “uphill” movement of Simulated Annealing algorithm, is performed to increase the diversity of the swarm and to guide the algorithm to escape from the local optimum. Simulation results show that the searching properties including searching efficiency, precision and robustness of IPSO algorithm are obviously better than that of the standard PSO(SPSO) algorithm.