摘要
针对基本微粒群优化(PSO,particle swarm optimization)算法存在早熟、易陷入局部极值等缺点,提出了一种改进的PSO优化算法。该算法分为全局搜索和局部搜索两个阶段。在全局搜索阶段采用基本PSO算法快速收缩搜索范围;在局部搜索阶段将PSO算法与模拟退火(SA,simulated annealing)算法结合,通过产生部分变异微粒确保算法能够跳出局部极值。同时为提高搜索效率,动态地减少种群规模。仿真结果表明,该算法具有较好的优化性能以及较高的执行效率。
An improved PSO algorithm is proposed for the disadvantages in the basic PSO algorithm such as premature convergence and easily trapping into local maxima.The improved algorithm is divided into overall and local search steps,in the first of which,basic PSO algorithm is used to decrease search space;in the second,the SA algorithm's thinking is injected to generate some worse particles and improve the search performance.In the same time,to heighten the search efficiency,the population size is dynamically reduced.The simulation results show that the improved PSO algorithm has better optimization performance and higher execution efficiency.
出处
《测控技术》
CSCD
北大核心
2010年第4期15-18,共4页
Measurement & Control Technology
基金
科技部国际科技合作项目(2007DFR10420)
重庆市重点科技攻关项目(CSTC2007AA2015
CSTC2008AC2107)
关键词
微粒群优化
模拟退火
动态种群规模
分段优化
particle swarm optimization
simulated annealing
dynamic population size
staged optimization