Research on Logistics Distribution Path Optimization Based on Improved Sparrow Search Algorithm
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.
logistics distribution path optimizationsparrow search algorithmparticle swarm technology
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