摘要
PSO是一种简单有效的随机全局优化技术,针对其容易陷入局部最优的缺点,论文将繁殖和退化操作引入微粒群算法。算法的主要特点是利用繁殖和退化,扩大搜索范围以提高收敛速度并保持种群的多样性。仿真程序表明,该算法能以较快速度完成给定范围的搜索和全局优化任务。
PSO is a simple stochastic global optimization technique,aiming at the shortcoming of it,that is,easily plunging into local minimum,the apomixis and degenerate operator is involved into particle swarm optimizer in this paper.The advantages are via apomixis and degenerate the speed of convergent and diversity of the swarm are improved apparently.The simulation results show that the algorithms can converge to the global optimum at quicker rate in a given range.
出处
《计算机工程与应用》
CSCD
北大核心
2006年第26期36-37,53,共3页
Computer Engineering and Applications
基金
国家自然科学基金资助项目(编号:60234030)
关键词
微粒群优化
繁殖
退化
变异
Particle Swarm Optiization,apomlxis,degenerate,mutation
