摘要
针对物流配送中心选址模型难以优化以至于影响配送效率的问题,提出一种基于非线性调节因子的猴群优化算法,对配送中心选址模型进行优化。针对传统猴群算法全局收敛精度低以及易早熟收敛陷入局部最优的问题,通过非线性调节因子和lateral变异策略对算法进行改进,提高了传统猴群算法的收敛精度和收敛速度。最后将改进后的猴群优化算法用于物流配送中心选址实验。
In order to solve the problem that it is difficult to optimize the location model of logistics distribution center,which affects the efficiency of distribution,a monkey optimization algorithm based on non-linear adjustment factors is proposed to optimize the location model of distribution center.Aiming at the problem of low global convergence accuracy and easy premature convergence falling into local optimum of traditional monkey algorithm,the algorithm is improved by nonlinear adjusting factor and late mutation strategy,which improves the convergence accuracy and speed of traditional monkey algorithm.Finally,the improved algorithm is used in the experiment of logistics distribution center location.
作者
李茂林
LI Maolin(Department of Electronic Information Engineering,Yuncheng Polytechnic College,Yuncheng 044000,China)
出处
《太原学院学报（自然科学版）》
2020年第2期44-50,共7页
Journal of TaiYuan University:Natural Science Edition
基金
国家自然科学基金(No.11805091)。
