Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (23): 61-67.DOI: 10.3778/j.issn.1002-8331.2002-0110

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Dynamic Parameter Adjustment Mechanism Based Self-Adaptive Cuckoo Search Algorithm

SONG Yu, SHI Libao   

  1. National Key Laboratory of Power Systems in Shenzhen(Shenzhen International Graduate School, Tsinghua University), Shenzhen, Guangdong 518055, China
  • Online:2020-12-01 Published:2020-11-30



  1. 电力系统国家重点实验室深圳研究室(清华大学深圳国际研究生院),广东 深圳 518055


In order to improve the convergence speed and accuracy of cuckoo search algorithm, an improved cuckoo search algorithm based on self-adaptive mechanism is proposed. The improved cuckoo search algorithm uses two different self-adaptive strategies at the beginning and end of iteration to adjust the step size and the discovery probability dynamically, aiming at improving the local and global optimization ability of the algorithm. Ten standard test functions are used to compare the performances of basic cuckoo search algorithm, the improved cuckoo search algorithm and other intelligent optimization methods. The results show that the improved cuckoo search algorithm has certain advantages in solution accuracy, stability and convergence speed.

Key words: computational intelligence, cuckoo search algorithm, self-adaptive strategy, global optimization



关键词: 计算智能, 布谷鸟算法, 自适应策略, 全局寻优