In this paper, a clustering cuckoo search optimization (CCSO) is proposed. Different from the randomly generated step size in CSO, the step size in CCSO is generated by a clustering mechanism, and the value is updated according to the average fitness value difference between each cluster and the whole swarm, thereby improving the searching balance between exploration and exploitation of each solution. The effectiveness of CCSO has been validated by six typical benchmark functions and economic load dispatch problems with 6, 10, 13, 15 and 40 generators. The results of CSO and CCSO are displayed and compared in aspects of convergence rate, objective function value and robustness. Moreover, the influences of parameters as step size \(\delta \), solution number P, egg abandon fraction \(p_a\) and cluster number K are all analyzed comprehensively in this study. The conclusion is that, in all the tested cases, CCSO behaves much more competitive than CSO under the same parameter setting conditions.

本文提出了一种布谷鸟杜鹃搜索优化算法（CCSO）。与CSO中随机生成的步长不同，CCSO中的步长是通过聚类机制生成的，并且根据每个聚类与整个群体之间的平均适应度差来更新该值，从而改善了探索和探索之间的搜索平衡。每种解决方案的利用。CCSO的有效性已通过6个典型基准功能和6、10、13、15和40台发电机的经济负荷分配问题得到了验证。显示并比较了CSO和CCSO的结果在收敛速度，目标函数值和鲁棒性方面。此外，参数的影响包括步长\（\ delta \），解数P和鸡蛋放弃分数本研究对\（p_a \）和簇数K进行了全面分析。结论是，在所有测试案例中，在相同的参数设置条件下，CCSO的性能都比CSO更具竞争力。