摘要
针对基本PSO算法存在易陷入局部最优点的缺点,提出了一种新型的PSO算法——混合变异粒子群算法。在每次迭代中,符合变异条件的粒子,以多种变异函数方式进行变异,而这些变异函数被赋予了一定概率,概率的划分取决于特定的优化问题。对几种典型函数的测试结果表明:在变异函数概率分配设置合适的情况下,混合变异粒子群算法增强了全局搜索能力,提高了搜索成功率,克服了基本PSO算法易于收敛到局部最优点的缺点,也明显优于单变异粒子群算法。
Aiming at the shortcoming of the standard PSO algorithm,that is easily plunging into local minimum,we propose a new Multi-Mutation Particle Swarm Optimization algorithm （MMPSO）.In each iteration,the particles which are satisfied the mutation condition are mutated with many functions,and each function is endowed a probability.The probability distribution relies on the specific optimization problem.The experimental results show that the MMPSO enhances the global searching ability and the probability of successful searching,and overcomes the standard PSO＇s liability to converge to local optimum.It is also superior to Single Mutation Particle Swarm Optimization algorithm（SMPSO）.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第7期59-61,181,共4页
Computer Engineering and Applications
基金
浙江省自然科学基金(the Natural Science Foundation of Zhejiang Province of China under Grant No.602161)
关键词
粒子群
变异
优化
particle swarm
mutation
optimization
