基于带变异的自适应精英改进蚁群算法的物流配送中心选址问题求解  

Solution to Location Problem of Logistics Distribution Center Based on Adaptive Elite Ant Colony Algorithm with Mutation

作　　者：李眩[1] 吴晓兵[1] 刘琼[1] LI Xuan;WU Xiaobing;LIU Qiong(Department of Economy and Trade,Tongling Vocational Technology College,Tongling 244061,China)

机构地区：[1]铜陵职业技术学院经贸系,安徽铜陵244061

出　　处：《成都大学学报（自然科学版）》2022年第1期46-51,共6页Journal of Chengdu University（Natural Science Edition）

基　　金：安徽省省级质量工程项目(2020mooc513);铜陵职业技术学院科学研究项目(tlpt2020NK016)。

摘　　要：建立以总成本最少同时满足时效性要求的物流配送中心选址问题的数学模型,并提出一种带变异的自适应精英改进蚁群算法对选址问题进行求解.本算法中的信息素挥发参数随迭代进行自适应调整,同时引入精英机制对群体当前解的信息素更新来进行有差别处理.此外,本算法移植了遗传算法的变异机制.当本算法陷入停顿时,对当前最优解施加扰动,将有效帮助本算法摆脱局部最优的束缚.验证结果表明,本算法对于实际中比较复杂的物流配送中心选址问题的求解结果较为理想,比基本蚁群算法在效率和寻优能力上有了进一步的提升.Based on the analysis of logistics distribution center location model,a logistics distribution center location model with the least total cost,which meets the timeliness requirements is established.Aiming at the practical optimization problem with high complexity,an adaptive elite ant colony algorithm with mutation is proposed to solve the location problem.The pheromone volatilization coefficient in the algorithm is adjusted adaptively with the iteration.At the same time,the elite mechanism is introduced to deal with the pheromone update of the current solution of the group differently.In addition,the mutation mechanism of GA algorithm is transplanted to the algorithm.When the algorithm comes to a standstill,it perturbs the current optimal solution to help the algorithm effectively get rid of the constraints of local optimization.The application results show that the adaptive elite ant colony algorithm with mutation has ideal results for solving the complex logistics distribution center location problem in reality,and has further enhanced the efficiency and the optimization capacity compared with the ordinary ant colony algorithm.

关 键 词：蚁群算法 优化 适应度 信息素 变异 精英 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] F253.9[自动化与计算机技术—控制科学与工程]