关键词: 混沌cat 映射, 灰狼优化算法, 函数优化, 高斯变异, 优胜劣汰选择

Abstract:  In order to overcome the defects of basic grey wolf optimization algorithm about the dependence on the initial population, the presence of premature convergence, and easily getting into local minima, this paper proposes an improved grey wolf optimization algorithm applied to solve the function optimization problem. In the proposed algorithm, firstly, chaotic Cat sequence is used to initiate individual position, which can strengthen the diversity of global searching. Secondly, the individual memory from PSO are applied to enhance the local search ability and convergence speed. Thirdly, a Gaussian disturbance based the rules of survival of the fittest will be given on the global optimum of each generation, thus the algorithm can effectively jump out of local minima. Experimental results based on the thirteen benchmark functions show that the proposed improved GWO algorithm is superior to the basic GWO, PSO, GA and ACO algorithm in both computational accuracy and convergence rate.

Key words: chaotic Cat map, grey wolf optimization algorithm, function optimization, Gaussian mutation, the rules of survival of the fittest