基于正余双弦自适应灰狼优化算法的医药物流配送路径规划  

Medical Logistics Distribution Path Planning Based on Sine Cosine and Adaptive Gray Wolf Optimization Algorithm

作　　者：石春花[1] 刘环 SHI Chun-hua;LIU Huan(Department of Biomedical Engineering Changzhi Medical College,Changzhi 046000,China;Department of Computer Teaching,Changzhi Medical College,Changzhi 046000,China)

机构地区：[1]长治医学院生物医学工程系,山西长治046000 [2]长治医学院计算机教学部,山西长治046000

出　　处：《数学的实践与认识》2020年第14期114-127,共14页Mathematics in Practice and Theory

基　　金：山西省高校科技开发项目(20091025);山西省高等学校科技创新项目(2019L0672);长治医学院博士启动基金项目(BS15015)

摘　　要：针对传统灰狼优化算法易早熟收敛陷入局部最优和收敛速度慢的缺陷,提出一种正余双弦自适应灰狼优化算法.首先,在灰狼捕食阶段引入正弦搜索,增强算法的全局勘探能力,减少算法的搜索盲点,提高算法的搜索精度.在引入正弦搜索的同时,引入余弦搜索,增强算法的局部开发能力,提高算法的收敛速度.其次,在搜索过程中加入自适应交叉变异机制,通过适应度值的大小自适应选取交叉变异概率,有效的提高了粒子跳出局部最优的概率.通过数值对比试验,验证了改进算法具有较强的收敛精度和收敛速度.Aiming at the defect that the traditional gray wolf optimization algorithm is easy to premature convergence and falls into local optimum and slow convergence rate,a positive cosine double-string adaptive gray wolf optimization algorithm is proposed.Firstly,the sinusoidal search is introduced in the grey wolf predation stage to enhance the global exploration ability of the algorithm,reduce the blind spot of the algorithm search,and improve the search accuracy of the algorithm.At the same time of introducing sinusoidal search,the cosine search is introduced to enhance the local development ability of the algorithm and improve the convergence speed of the algorithm.Secondly,the adaptive cross-mutation mechanism is added in the search process,and the cross-mutation probability is adaptively selected by the size of the fitness value,which effectively improves the probability of the particle jumping out of the local optimum.Through numerical comparison experiments,it is verified that the improved algorithm has strong convergence precision and convergence speed.

关 键 词：灰狼优化算法 正余双弦 自适应交叉变异 元启发优化算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]