The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence spe...The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings,a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy(CLSSA)is proposed in this paper.Firstly,in order to balance the exploration and exploitation ability of the algorithm,chaotic mapping is introduced to adjust the main parameters of SSA.Secondly,in order to improve the diversity of the population and enhance the search of the surrounding space,the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally,the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions,and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map,and CLSSA is efficient and useful for engineering problems,which is better than all comparison algorithms.展开更多
针对现有多车场车辆路径问题研究多局限于同质商品配送的现状,提出考虑车场商品库存差异与客户多商品需求的多车场协同配送车辆路径问题(multi-depot collaborative distribution vehicle routing problem with multi-commodity,MDCDVRP...针对现有多车场车辆路径问题研究多局限于同质商品配送的现状,提出考虑车场商品库存差异与客户多商品需求的多车场协同配送车辆路径问题(multi-depot collaborative distribution vehicle routing problem with multi-commodity,MDCDVRPMC),通过订单拆分处理异构需求,构建以运输成本最小化为目标的混合整数规划模型,并设计增强型自适应大邻域搜索(enhanced adaptive large neighborhood search,EALNS)算法进行求解。该算法融合K-means聚类、节约算法和贪婪重组策略生成初始解,采用自适应大邻域搜索算法避免早熟收敛,结合2-opt邻域操作与模拟退火Metropolis准则实现深度优化。最后,采用Gurobi求解器与自适应大邻域搜索(ALNS)、遗传算法(genetic algorithm,GA)和蚁群算法(ant colony optimization,ACO)进行标准案例测试,验证模型正确性与算法性能。结果表明:EALNS在保证解质量的前提下,求解效率显著提升(求解时间仅为Gurobi的2%);相较于对比算法,其求解质量提升13%~35%,解稳定性提高20%~40%,展现出更优的收敛性能和鲁棒性。研究成果为复杂物流环境下多车场的协同配送提供了高效解决方案,有效拓展了车辆路径优化理论在实际物流场景中的应用范围。展开更多
基金The Science Foundation of Shanxi Province,China(2020JQ-481,2021JM-224)Aero Science Foundation of China(201951096002).
文摘The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings,a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy(CLSSA)is proposed in this paper.Firstly,in order to balance the exploration and exploitation ability of the algorithm,chaotic mapping is introduced to adjust the main parameters of SSA.Secondly,in order to improve the diversity of the population and enhance the search of the surrounding space,the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally,the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions,and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map,and CLSSA is efficient and useful for engineering problems,which is better than all comparison algorithms.
文摘针对现有多车场车辆路径问题研究多局限于同质商品配送的现状,提出考虑车场商品库存差异与客户多商品需求的多车场协同配送车辆路径问题(multi-depot collaborative distribution vehicle routing problem with multi-commodity,MDCDVRPMC),通过订单拆分处理异构需求,构建以运输成本最小化为目标的混合整数规划模型,并设计增强型自适应大邻域搜索(enhanced adaptive large neighborhood search,EALNS)算法进行求解。该算法融合K-means聚类、节约算法和贪婪重组策略生成初始解,采用自适应大邻域搜索算法避免早熟收敛,结合2-opt邻域操作与模拟退火Metropolis准则实现深度优化。最后,采用Gurobi求解器与自适应大邻域搜索(ALNS)、遗传算法(genetic algorithm,GA)和蚁群算法(ant colony optimization,ACO)进行标准案例测试,验证模型正确性与算法性能。结果表明:EALNS在保证解质量的前提下,求解效率显著提升(求解时间仅为Gurobi的2%);相较于对比算法,其求解质量提升13%~35%,解稳定性提高20%~40%,展现出更优的收敛性能和鲁棒性。研究成果为复杂物流环境下多车场的协同配送提供了高效解决方案,有效拓展了车辆路径优化理论在实际物流场景中的应用范围。
