摘要
针对标准粒子群优化算法在优化多极值函数时容易陷入局部最优的缺点,分析了其进化原理以及过早收敛的原因,并在此基础上提出了分阶段进化的改进算法,即将进化过程分成多个阶段,不同的进化阶段应用不同的迭代进化公式,以提高种群的多样性,进而有效避免过早收敛。仿真实验结果表明,对于复杂的多极值函数优化问题,改进后的方法比标准粒子群优化算法具有更强的全局寻优性能。
In view of the shortcomings of the standard particle swarm optimization algorithm （PSO） easily falling into local optimization when solving multi-extreme value function, a multi-stages optimi- zation algorithm is presented through analyzing the evolution principle and the reason of premature convergence in this paper. The improved algorithm divides the evolution process into multi-stages. In addition, a different fit iterative formula is adopted in each stage, so the population diversity can be increased and the premature convergence ean be effectively avoided accordingly. Simulation results show that the improved algorithm has better global extreme function problems optimization capability than standard PSO on multi-
出处
《重庆理工大学学报（自然科学）》
CAS
2012年第6期67-70,共4页
Journal of Chongqing University of Technology：Natural Science
基金
国家自然科学基金资助项目(60973096)
航空科学基金资助项目(2010ZC56007)
周口师范学院青年科研基金资助项目(2012QN04
2012QN01)
关键词
粒子群优化算法
局部最优
种群多样性
particle swarm optimization algorithm
local optimization
population diversity
