摘要
提出了一种新的带变异算子的粒子群优化算法(MOPSO).该算法通过在后期引入变异算子,有效地增强了粒子群优化(PSO)算法跳出局部最优解的能力,且使PSO算法既摆脱了后期易陷入局部最优点的束缚,又保持了其前期搜索速度快的优点.
This paper presents a new mutation operator particle swarm optimization （MOPSO）. By adding the mutation operator to the algorithm in the later phase of convergence, this algorithm improves the ability of particle swarm optimization （PSO） algorithm to break away from the local optimum solutions effectively, makes the PSO algorithm not only escape from the possibility of getting into local minimum＇ s basin of attraction at the later phase, but also maintain the advantages of fast searching speed in the early convergence phase.
出处
《重庆工学院学报》
2005年第8期38-40,共3页
Journal of Chongqing Institute of Technology
基金
国家自然科学基金项目资助(60374063)
宝鸡文理学院院级科研计划项目(JK2439)
宝鸡文理学院中青年科研计划项目(QK2407).
关键词
约束规划
粒子群
变异算子
constraint programming
particle swarm
mutation operator
