混沌差分进化粒子群协同优化算法  

Chaotic Differential Evolution Particle Swarm Cooperative Optimization Algorithm

作　　者：匡芳君[1,2] 张思扬[2] 金忠[1] 徐蔚鸿[1,3] 

机构地区：[1]南京理工大学计算机科学与工程学院,江苏南京210094 [2]湖南安全技术职业学院电气与信息工程系,湖南长沙410151 [3]长沙理工大学计算机与通信工程学院,湖南长沙410114

出　　处：《微电子学与计算机》2014年第8期29-33,39,共6页Microelectronics & Computer

基　　金：国家自然科学基金资助项目(61373063);湖南省科技计划项目(2012SK4046;2013FJ4217);湖南省教育厅资助科研项目(13C086)

摘　　要：为有效地改善差分进化粒子群算法的性能,结合反向学习策略和信息交互机制,提出了一种新的混沌差分粒子群协同优化算法.该算法采用反向学习策略产生初始种群,使得初始个体尽可能均匀分布,然后将初始种群随机等分为双种群,对双种群分别采用改进的混沌差分进化算法和混沌粒子群优化算法进行协同寻优,并在双种群中引入信息交互学习机制,在维持种群多样性的同时加快收敛速度.通过对四个复杂高维的标准函数寻优测试,仿真结果表明,该算法能有效避免早熟收敛,收敛速度快,寻优精度较高,具有良好的全局搜索能力,鲁棒性好.To improve the performance of differential evolution particle swarm optimization , a novel chaotic differential evolution particle swarm cooperative optimization algorithm is proposed , which is combined the opposition-based learning and the interactive learning strategy .In this algorithm ,an initialization strategy based on the opposition-based learning is applied to diversify the initial individuals in the search space .All individuals are randomly divided into two sub-swarm ,one sub-swarm searches via improved chaotic differential evolution ,and the other searches via improved chaotic particle swarm optimization at the same time .The interactive learning strategy is introduced in the bi-group to maintain the population diversity and accelerate the convergence speed .Experiments on four complex benchmark functions with high dimension ,simulation results further demonstrate that ,the algorithm not only effectively avoids the premature convergence , but also has rapid convergence speed , high solution precision ,good searching ability and robustness .

关 键 词：差分进化 粒子群优化 混沌搜索 协同优化 反向学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]