摘要
针对粒子群优化算法在优化多极值点复杂问题时容易陷入局部极值的不足,提出一种新的分阶段进化的粒子群优化算法。该方法进化过程分为两个阶段,每个阶段对应一个不同的模型,通过结合这两种模型的各自优点有效地降低群体陷入局部最优。仿真实验结果表明,对于复杂多极值函数优化问题,本文算法比标准粒子群算法的寻优能力更强。
Considering that the standard PSO easily falls into local optimization when it solves the multi-extremum problems,an improved PSO algorithm is proposed.In this algorithm,the evolution process is divided into two stages,while each stage uses a different model,so the possibility of getting local extreme value is reduced by making full use of the respective advantages of the two evolution model.The results of simulation show that the proposed algorithm has the better optimization performance than the standard PSO when solving the multi-extremum problems.
出处
《计算机与现代化》
2012年第4期152-154,共3页
Computer and Modernization
关键词
粒子群优化算法
局部极值
进化模型
particle swarm optimization algorithm
local extremum
evolution model
