一个与信籁域搜索技术相结合的微粒群算法

A Particle Swarm Optimization Algorithm Combined with Trust Region Searching

微粒群优化算法(PSO)是一种有效的随机全局优化技术.文章针对利用微粒群优化算法进行多极值点的函数优化时,存在陷入局部极小点和搜索效率低的问题,把信籁域搜索技术引入到PSO算法中,提出了基于信籁域搜索的微粒群优化算法(TRPSO).该算法保持了PSO算法结构简单的特点,改善了PSO算法的全局寻优能力,提高了算法的收敛速度和计算精度.仿真计算结果表明,该算法的性能优于混沌微粒群优化算法(CPSO)和基本微粒群优化算法(PSO).

Particle swarm optimization（PSO） algorithm is one of the most powerful methods for solving unconstrained and constrained global optimization problems.This paper,thinking of PSO algorithm is easy to trap into local minima in solving multimodal function,incorporates trust region searching into the PSO algorithm,and propose a new particle swarm optimization algorithm combined with trust region searching（TRPSO）.The proposed algorithm has not only the simple for implement of algorithm PSO,also the fast convergence and high computational precision.The computational results on several benchmark problems have shown the proposed algorithm is superior to CPSO and PSO.