一种基于混沌搜索和鲶鱼效应策略的粒子群算法  

PARTICLE SWARM OPTIMISATION ALGORITHM BASED ON CHAOTIC SEARCH AND CATFISH EFFECT STRATEGY

作　　者：易文周[1] 田立伟[2] 

机构地区：[1]广东工程职业技术学院计算机信息系,广东广州510520 [2]广东科技学院计算机系,广东东莞523083

出　　处：《计算机应用与软件》2013年第5期311-315,共5页Computer Applications and Software

摘　　要：为了提高粒子群优化算法的寻优精度,分析了最新文献提出的三种粒子群算法改良策略的优点和不足之处,对混沌搜索策略和鲶鱼效应策略进行了改良,通过仿真证明了提出的改良方法的优越性。最后提出一种新型混合算法,并在仿真实验中进行了各种算法性能比较,验证了最终提出的新型混合算法有更好的优化性能。In order to improve the optimisation accuracy of particle swarm optimisation algorithm,we analyse the advantages and disadvantages of the three improvement strategies for particle swarm algorithm presented by the latest literatures,make the improvements on chaos search strategy and catfish effect strategy,and prove the superiority of these two improved methods with simulation.In end of the paper,we present a novel hybrid algorithm,and carry out performances comparison on various algorithms in simulative experiment,and verify that this new hybrid algorithm put forward at the last has better optimisation performance.

关 键 词：粒子群优化算法 差分进化算法 混合算法 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]