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

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.