求解烟草配送路径规划问题的新型智能优化算法  

A novel intelligent optimization algorithm for tabaco distribution routing problem

作　　者：高怡杰 何湘竹[1] 石英 王建树[2] GAO Yijie;HE Xiangzhu;SHI Ying;WANG Jianshu(College of Electronics and Information Engineering,South-Central University for Nationalities,Wuhan 430074,China;Department of Logistics,Hubei Tobacco Company,Wuhan 430074,China)

机构地区：[1]中南民族大学电子信息工程学院,武汉430074 [2]湖北省烟草公司物流处,武汉430074

出　　处：《中南民族大学学报（自然科学版）》2022年第1期87-93,共7页Journal of South-Central University for Nationalities：Natural Science Edition

基　　金：中央高校基本科研业务费专项资金资助项目(CZY19002)。

摘　　要：提出了一种求解烟草配送路径规划问题的新型智能优化算法ITLBO.受现代多样化学习方式的启发,在传统教与学优化(TLBO)算法的框架基础上,新增加了培训阶段、自学阶段和反向学习阶段,以提高算法的全局寻优能力和解的质量.引入迭代变化法、线性顺序交叉(LOX)、2-opt算子对每个学习阶段离散化,使得算法能很好适用于组合优化问题.混合了精英选择、自适应退火以及禁忌策略,在有效平衡种群集中性和多样性的同时,加快算法的搜索过程.对某烟草公司单一车辆和多车辆配送路径规划问题求解结果表明:所提出的算法能优化配送路线,降低配送成本.An improved teaching-learning-based optimization(ITLBO)algorithm is proposed to handle the vehicle routing problem. Inspired by the various modern learning style. Three novel phases namely training,self-learning and inverselearning are introduced into the framework of the traditional TLBO to enhance the exploration ability as well as the optimal quality of the algorithm. The iterative transformation method,the linear order crossover and the 2-opt operator are adopted to discrete the learning process,which makes the ITLBO suitable for the combinatorial optimization problem. Aiming at achieving a balance between the intensity and diversity of the species,as well as speeding up the convergence,the elite selection,adaptive simulated annealing,taboo strategy are integrated together. The tests on tobacco distribution examples are carried out,and the results verify the effectiveness of the proposed algorithm in solving vehicle routing problems.

关 键 词：教与学优化算法 车辆路径问题 线性顺序交叉 迭代变换法 自适应模拟退火机制 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]