改进布谷鸟算法求解多维复杂函数优化问题  

Improving the Cuckoo Search Algorithm to Solve Multidimensional Complex Function Optimization Problems

作　　者：刘晓珍 毛艺楠 LIU Xiao-zhen;MAO Yi-Nan(Zhengzhou Technical College,Zhengzhou 450010,China;Henan University of Engineering,Zhengzhou 450010,China)

机构地区：[1]郑州职业技术学院,河南郑州450010 [2]河南工程学院,河南郑州450010

出　　处：《电脑与电信》2025年第1期23-26,31,共5页Computer & Telecommunication

基　　金：河南省高等学校重点科研项目,项目编号:24B520047。

摘　　要：针对布谷鸟算法的不足之处,提出一种融合差分变异机制及瑞利分布的自适应布谷鸟算法(DERCS)。初始化种群时引入让鸟巢位置分布更加均匀的Logistic混沌映射;全局搜索阶段,引入具有瑞丽分布的步长缩放因子,使鸟巢位置移动具有自适应性,增强算法的搜索能力;局部搜索阶段,融合差分定向变异策略,根据决策随机数与发现概率的比较,采取不同方向的位置移动,有效提高局部搜索的探测能力。通过仿真实验验证,DERCS算法的整体寻优性能得到提升,稳定性进一步增强。Aiming at the shortcomings of the Cuckoo Search Algorithm,an adaptive Cuckoo Search Algorithm that integrates differ‐ential mutation mechanism and Rayleigh distribution(DERCS)is proposed.When initializing the population,introducing logistic cha‐otic mapping to make the distribution of bird nest positions more uniform.In the global search phase,a step size scaling factor with a Rayleigh distribution is introduced to make the position movement of the bird's nest adaptive and enhance the search capability of the algorithm.In the local search stage,the fusion of differential directional mutation strategy is used to effectively improve the de‐tection capability of local search by adopting different directions of position movement based on the comparison between decision random number and discovery probability.Through simulation experiments,it has been verified that the overall optimization perfor‐mance of the DERCS algorithm has been improved,and its stability has been further enhanced.

关 键 词：布谷鸟算法 瑞丽分布 步长缩放因子 差分定向变异机制 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]