融合翻筋斗觅食和正余弦策略的白骨顶鸡优化算法  

Coot Optimization Algorithm Integrating Somersault Foraging and Sine-cosine Strategy

作　　者：张志飞 孔维宾[1] 杜义 张婷琳 王玉婷 高薪越 ZHANG Zhifei;KONG Weibin;DU Yi;ZHANG Tinglin;WANG Yuting;GAO Xinyue(School of Information Engineering,Yancheng Institute of Technology,Yancheng 224051,China)

机构地区：[1]盐城工学院信息工程学院,江苏盐城224051

出　　处：《软件工程》2024年第11期44-48,共5页Software Engineering

基　　金：江苏省研究生实践创新计划项目(SJCX23-1871,SJCX23-XY069,SJCX23-XY071);2023年大学生创新创业训练计划项目(2023591,2023576,2023039)。

摘　　要：针对白骨顶鸡优化算法(COOT)全局搜索能力差、优化精度低和易陷入局部最优等缺陷,提出了一种融合翻筋斗觅食和正余弦策略的白骨顶鸡优化算法,引入一种翻筋斗觅食策略,增加了白骨顶鸡个体的多样性。此外,在领导者位置更新过程中采用正余弦策略和随机因子,以提高算法的搜索能力。通过10个标准检验函数,以及Wilcoxon秩和检验,对改进的COOT性能进行了全面评估,并将其与其他几种算法进行了比较。仿真结果表明,改进的COOT在迭代速度和收敛精度方面取得了显著的提升,并展现了出色的鲁棒性。Aiming at the limitations of the Coot Optimization Algorithm(COOT)in terms of poor global search ability,low optimization accuracy,and susceptibility to getting stuck in local optima,a COOT algorithm integrating somersault foraging and sine-cosine strategy is proposed.This algorithm integrates somersault foraging and sine cosine strategy.By introducing a somersault foraging strategy,the individual diversity of the COOT bird is increased.Additionally,sine-cosine strategy and a random factor are employed during the leader position update process to enhance the algorithm's search capability.The performance of the improved COOT is comprehensively evaluated using ten standard benchmark functions and the Wilcoxon rank sum test,and the results are compared with several other algorithms.Simulation results indicate that the improved COOT has achieved significant enhancements in iteration speed and convergence accuracy,demonstrating excellent robustness.

关 键 词：改进白骨顶鸡优化算法 翻筋斗觅食策略 正余弦策略 随机因子 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]