Tent混沌人工蜂群与粒子群混合算法  

Hybridization algorithm of Tent chaos artificial bee colony and particle swarm optimization

作　　者：匡芳君[1,2] 金忠[1] 徐蔚鸿[1,3] 张思扬[2] 

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

出　　处：《控制与决策》2015年第5期839-847,共9页Control and Decision

基　　金：国家自然科学基金项目(61373063;61233011;61402227);湖南省科技计划项目(2013FJ4217)

摘　　要：针对人工蜂群和粒子群算法的优势与缺陷,提出一种Tent混沌人工蜂群粒子群混合算法.首先利用Tent混沌反向学习策略初始化种群;然后划分双子群,利用Tent混沌人工蜂群算法和粒子群算法协同进化;最后应用重组算子选择最优个体作为跟随蜂的邻域蜜源和粒子群的全局极值.仿真结果表明,该算法不仅能有效避免早熟收敛,而且能有效跳出局部极值,与其他最新人工蜂群和粒子群算法相比具有较强的全局搜索能力和局部搜索能力.In view of the advantages and disadvantages of artificial bee colony（ABC） algorithm and particle swarm optimization（PSO） algorithm, a hybridization algorithm of Tent chaos artificial bee colony and particle swarm optimization （HTCAP） is proposed. In the HTCAE an initialization strategy based on Tent chaotic opposition-based learning is applied. All individuals are divided into two sub-swarms by cooperative evolution with Tent chaos artificial bee colony（TCABC） algorithm and Tent chaos particle swarm optimization（TCPSO） algorithm. The best solution obtained by the recombination operator is as the neighbor food source for onlooker bees and the global best of particle swarm, respectively. Simulation results show that, the algorithm not only effectively avoids the premature convergence, but also gets rid of the local minimum. By comparison with the other latest algorithms based on the ABC algorithm and PSO algorithm, the proposed model has better global and local searching abilities.

关 键 词：Tent混沌搜索 人工蜂群算法 粒子群优化算法 混沌反向学习 重组算子 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]