摘要
为了提高基本布谷鸟搜索算法求解多维函数优化问题的性能,提出一种基于局部搜索增强策略的自适应反向学习布谷鸟算法.在基本CS算法完成Levy Flights全局寻优后,采用局部搜索增强策略更新种群空间,增加种群多样性,避免算法陷入局部最优;自适应发现概率用于调节全局寻优和局部求精之间的平衡;反向学习扰动机制嵌入布谷鸟算法作为局部开采技术,增加算法找到最优解的概率.实验结果表明,改进算法能够有效地提高CS算法的收敛速度并改善解的质量,具有一定的鲁棒性.
In order to improve the performance of basic cuckoo search algorithm in solving multi-dimensional function optimization problems,an adaptive Opposition-Based learning cuckoo algorithm based on local search enhancement strategy is proposed.After performing the Levy flight global optimization,the local search strategy is used to update the population space to increase the diversity of the population,and avoid the algorithm falling into the local optimum;the adaptive discovery probability is used to adjust the balance between the global optimization and the local accuracy;the Opposition-Based learning disturbance mechanism is embedded in the cuckoo algorithm as the local exploitation technology to increase the probability of finding the best solution.The simulation experiments show that the proposed algorithm can improve the convergence speed and the quality of the solutions effectively it has certain robustness.
作者
张燕
袁书卷
达列雄
周军
ZHANG Yan;YUAN Shu-juan;DA Lie-xiong;ZHOU Jun(School of Mathematics and Computer Science,Shaanxi University of Technology,Hanzhong 723000,China;School of Education Science,Shaanxi University of Technology,Hanzhong 723000,China)
出处
《数学的实践与认识》
北大核心
2020年第20期191-200,共10页
Mathematics in Practice and Theory
基金
陕西理工大学科研基金项目(SLGKY2017-07)。
关键词
布谷鸟搜索算法
反向学习
自适应
局部搜索策略
cuckoo search algorithm
opposition-Based learning
adaptive
local search strat
