基于改进麻雀搜索算法的物流配送路径优化研究

Research on Logistics Distribution Path Optimization Based on Improved Sparrow Search Algorithm

在当前竞争激烈的市场环境中,优化物流配送路径成为企业控制供应链成本、提高服务效率和客户满意度的关键。针对供应链环节中物流配送成本高和时间长等痛点,文中构建了一个物流配送路径优化模型,旨在最大程度地减少配送时间和成本。以麻雀搜索算法(Sparrow Search Algorithm,SSA)为基础框架,引入Cubic混沌映射来初始化种群,旨在增进种群初始位置的多样性,并促进算法跳出局部最优解。在迭代过程中,模型通过重心反向学习机制对麻雀个体进行适时变异,从而提升了算法的全局搜索能力并有效防止了早熟收敛的问题。随后,引入粒子群技术以提高算法的寻优精度和稳定性。最后,经过一系列实验验证,该算法在优化搜索方面有较好的优越性。

In the current market environment with fierce competition,optimizing logistics distribution paths has become the key for enterprises to control supply chain costs,and improve service efficiency and customer satisfaction.In view of the pain points of high logistics distribution costs and long time in the supply chain,this paper constructs a logistics distribution path optimization model,aiming to minimize distribution time and costs.It uses the sparrow search algorithm(SSA)as the basic framework and introduces Cubic chaos mapping to initialize the population,which is intended to increase the diversity of the initial position of the population and promote the algorithm to jump out of the local optimal solution.During the iterative process,the model mutates individual sparrows in a timely manner through a center-of-gravity reverse learning mechanism,thereby improving the global search capability of the algorithm and effectively preventing premature convergence.And then particle swarm technology is introduced to improve the optimization accuracy and stability of the algorithm.Finally,after a series of experimental verifications,it is found that the algorithm has good advantages in optimization search.