基于模拟退火算法思想的粒子群优化算法

PARTICLE SWARM OPTIMIZATION BASED ON THE IDEAL OF SIMULATED ANNEALING ALGORITHM

粒子群优化是由Eberhart博士和Kennedy博士于 1995年根据鸟或鱼群居社会行为而提出的。本文提出了 4种改进的算法 ,特别推荐结合模拟退火算法思想提出的一种新算法。经过与基本粒子群算法比较测试 ,证实它是一种简单有效的算法。

Particle swarm optimization(PSO)is an evolutionary computation technique developed by Dr.Eberhart and Dr.Kennedy in 1995,inspired by social behavior of bird flocking or fish schooling.In this paper four improved methods are given.The particle Swarm optimization algorithm combines the ideal of the simulated annealing algorithm are recommended.The new algorithms are tested and compared with the standard PSO.It is proved that a new method is a simple and effective algorithm.