一种基于鲶鱼效应和新型搜索机制的混沌蝙蝠算法

Chaotic bat algorithm based on chaos catfish effect and new search mechanism

针对蝙蝠算法(BA)收敛速度慢、易早熟、寻优精度差的缺点,该文提出一种基于鲶鱼效应和新型搜索机制的改进的混沌蝙蝠算法。首先采用均匀性更好的Tent混沌序列产生初始种群,以增强初始种群多样性。设计了新型频度和速度更新函数,以更好地调节种群的聚集速度,提高全局搜索能力,缓解局部最优现象。将混沌扰动思想引入蝙蝠算法,提出一种新的局部搜索机制和变步长搜索策略,以提高局部搜索的效率和精度。设计了基于混沌鲶鱼效应的种群激活机制,增强了蝙蝠群体跳出局部最优和加速收敛的能力。典型函数的对比测试结果证明了该算法的有效性。

In view of that the bat algorithm(BA)has disadvantages of slow convergence,prematurity and poor precision,an improved bat algorithm based on the chaos catfish effect and the new search mechanism is proposed here.Firstly,the tent chaotic sequence is used to generate initial population to enhance its diversity.The new pulse emission rate and the velocity update function are designed to adjust the aggregation speed of the population better and alleviate the local optimum phenomenon.Secondly,the new local search mechanism and the variable step size search strategy are introduced to improve the efficiency and accuracy of local search.Thirdly,the activation mechanism of the bat population based on the chaotic catfish effect is designed to enhance the ability of the algorithm to jump out of the local optimum and accelerate convergence.The test results of typical functions demonstrate the effectiveness of the proposed algorithm.