摘要： 在基本人工鱼群算法的后期,人工鱼会有极大可能聚集在非全局最优值处,导致算法陷入局部最优,从而使得基本鱼群算法在搜索精度、收敛速度及稳定性等方面受到严重影响。为克服上述缺点,在自适应Levy分布的基础上,提出一种混合变异改进人工鱼群算法。利用Levy分布及混沌变异的特点,增加基本人工鱼群算法中人工鱼状态的多样性,提高基本人工鱼群算法跳出局部最优的能力。对基本鱼群算法进行改进,通过典型测试函数的实验仿真结果表明,与粒子群算法及人工鱼群算法相比,改进人工鱼群算法的寻优能力更强。

关键词: 人工鱼群算法, Levy分布, 混沌, 变异, 函数优化

Abstract: In the late stage of basic artificial fish swarm algorithm, artificial fish has great potential to be aggregated in a non global optimum, causing the algorithm to fall into local optimum, thus the artificial fish swarm algorithm is seriously affected in search accuracy, convergence speed and stability. In order to overcome this shortcoming, an improved adaptive Levy distribution mixed mutation artificial fish swarm algorithm is proposed. By the characteristics of Levy distribution and chaotic mutation, the diversity of artificial fish in basic artificial fish swam algorithm is increased, which improves the ability of the basic artificial fish swarm algorithm to jump out of local optimum, so as to improve the basic fish swarm algorithm. Simulation results of the typical test function show the improved artificial fish swarm algorithm has better optimization ability than the particle swarm optimization algorithm and artificial fish swarm algorithm.

Key words: artificial fish swarm algorithm, Levy distribution, chaos, mutation, function optimization

中图分类号: 

• TP301.6