关键词: 布谷鸟搜索算法, 自适应, 精英策略, 引力搜索, 高斯扰动

Abstract: In order to solve the problem of Cuckoo Search（CS） algorithm, a new Elite Cuckoo Search algorithm with Gravitational search and Gauss perturbation（GGECS） is proposed. In this algorithm, a self-adaptive control strategy is put forward to adjust step size factor and discovery probability dynamically in cuckoo algorithm. Besides, the population is classified by using the Pareto principle. Gravity search and Gaussian perturbation are applied to the bird’s nests which belong to different population. By this method, the population diversity of the algorithm is improved. It can avoid falling into a local optimal solution, improve the searching precision and accelerate the convergence speed. This algorithm uses eight benchmark test functions to carry out simulation experiments. The results show GGECS algorithm has better global search capability than CS algorithm and Improved Cuckoo Search（ICS） algorithm. The results from GGECS algorithm are more close to theory optimal solution.

Key words: Cuckoo Search（CS） algorithm, self-adaptation, elite strategy, gravitational search, Gaussian perturbation