基于模拟退火的改进混沌粒子群算法

Improved Chaotic Particle Swarm Optimization Algorithm Based on Simulated Annealing

针对传统的粒子群优化算法易陷入局部最优解、稳定性差等缺陷.本文提出了基于模拟退火的改进混沌粒子群算法.将模拟退火及混沌的算法应用于粒子运动过程,从而可有效避免陷入局部最优并趋于全局最优.仿真结果表明在最优解精度以及寻优速度上都有一定提高.

The traditional part icle swarm opt imizat ion algorithm is easy to plung into local opt imal solution and the poor stability . In this paper, an improved chaotic particle swarm optimization algo-rithm based on simulated annealing is proposed. The algorithm of simulating annealing and chaso is ap-plied to the process of particle motion,which can effectively avoid getting into local optimal and getting the optimal global optimization. The results show that the optimal solution accuracy and the optimum speed are improved.