关键词: 布谷鸟搜索算法, 收敛性, 动态参数, 全局搜索, 基准测试

Abstract: Cuckoo Search algorithm（CS） is a swarm intelligence optimization algorithm which is neoteric and inspired by biology. To overcome the defections that standard algorithm has slow convergence rate and the search for algorithm optimization ability is weak, a new Adaptive Probability of Cuckoo Search algorithm（APCS） is proposed. Firstly, a parameter called state discriminant [Ps] is proposed and calculated by using Pareto optimal solution. Secondly, an equilibrium parameters [Peb] is created and calculated by exploration and exploitation equilibrium state. Finally, the adaptive dynamic adjustment of the discovery probability [Pa] is realized. In the end, eight benchmark functions are used to compare and analyze the performance of the two algorithms in 10 and 30 dimensions. Experiment results show that the convergence speed, optimization ability, stability and calculation time of APCS algorithm are better than those of CS algorithm.

Key words: cuckoo search algorithm, convergence, dynamic parameters, global search, benchmarks