基于柯西变异和差分进化的混沌白骨顶鸟算法  

Chaos COOT Bird Algorithm Based on Cauchy Mutation and Differential Evolution

作　　者：周雪荃 杜逆索 欧阳智 ZHOU Xuequan;DU Nisuo;OUYANG Zhi(School of Mathematics and Statistics,Guizhou University,Guiyang 550025,China;Guizhou Big Data Academy,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学数学与统计学院,贵阳550025 [2]贵州省大数据产业发展应用研究院(贵州大学),贵阳550025

出　　处：《计算机科学》2023年第8期209-220,共12页Computer Science

基　　金：贵州省科学技术厅重大科技计划项目(黔科合重大专项字[2018]3002);贵州大学培育项目(贵大培育[2020]41号)。

摘　　要：针对白骨顶鸟优化算法(COOT)寻优精度低、容易陷入局部最优、收敛速度慢等问题,提出了基于柯西变异和差分进化的混沌白骨顶鸟算法(Logistic Chaos Coot bird algorithm based on Cauchy mutation and Differential evolution,CDLCOOT)。首先,通过柯西变异使白骨顶鸟位置发生扰动,扩大搜索范围,提高算法的全局搜索能力;其次,对领导者白骨顶鸟采取差分进化策略,增加种群多样性,使适应度更好的领导者带领种群寻优,引导白骨顶鸟个体向最优解前进,帮助其更快地搜索;最后,在白骨顶鸟进行链式运动时加入logistic混沌因子,从而实现混沌的链式跟随运动,提高算法跳出局部最优的能力。在12个经典的测试函数和9个CEC2017测试函数上进行仿真实验,将CDLCOOT算法与正余弦算法(SCA)、灰狼优化算法(GWO)、蚁狮优化算法(ALO)、黑洞模拟算法(MVO)等其他先进算法及原始COOT算法、具有单一策略的原算法进行对比,验证改进算法的有效性。实验结果表明,CDLCOOT算法相比其他启发式算法和改进算法具有更好的全局寻优能力和更快的收敛速度。在经典测试函数中,对于4个单模态函数,CDLCOOT算法寻优平均值相比原始算法平均提高了76个数量级;在2个多模态函数上寻到理论最优值,在另外2个多模态函数上寻优平均值分别比原始算法提高了三四个数量级;在4个固定维度多模态函数上,算法都能寻到理论最优值,收敛速度更快。在CEC2017测试函数中,所提算法在单模态、多模态和混合模态上的收敛精度相比原算法都有所提升,且其收敛速度也比原算法和其他算法更快,算法稳定性更高。Aiming at the problems of low optimization accuracy,easy to fall into local optimization and slow convergence speed of COOT bird algorithm,a logistic chaos CDLCOOT algorithm based on Cauchy mutation and differential evolution is proposed.Firstly,the position of the COOT bird is disturbed by Cauchy mutation to expand the search range and improve the global search ability of the algorithm.Secondly,the differential evolution strategy is adopted for the leader COOT bird to increase the population diversity,so that the leader with better fitness can lead the population to search for the optimal solution,guide the individual COOT bird to move towards the optimal solution,and help it search faster.Finally,the logistic chaos factor is added to the chain movement of the COOT bird,so as to realize the chaotic chain following movement and improve the ability of the algorithm to jump out of the local optimum.Simulation experiments are carried out on 12 classical test functions and 9 CEC2017 test functions.The CDLCOOT algorithm is compared with other advanced algorithms,such as the sine cosine algorithm(SCA),gray wolf optimizer(GWO),ant lion optimizer(ALO),multi-verse optimizer(MVO),as well as original COOT bird algorithm and the original algorithm with single strategy to verify the effectiveness of the improved algorithm.Experimental results show that CDLCOOT has better global optimization ability and faster convergence speed than other heuristic algorithms and improved algorithms.In the classical test functions,the average value of the algorithm is 76 orders of magnitude higher than that of the original algorithm on the four unimodal functions.The theoretical optimal value is found on two multimodal functions,and the average value on other two multimodal functions is 3 or 4 orders of magnitude higher than the original algorithm.On the four fixed dimension multimodal functions,the algorithm can find the theoretical optimal value,and the convergence speed is faster.In CEC2017 test functions,the optimization accuracy of the al

关 键 词：白骨顶鸟算法 柯西变异 差分进化 Logistic混沌 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]