Research On Logistics Distribution Center Location Based On Improved Paeticle Swarm Optimization

At the moment of rapid economic development,distribution centers are both an important hub for the distribution of goods and a key node in the logistics supply chain.In the new economic form,the logistics distribution center plays a role in the allocation of physical goods and integrates the basic functions of order processing,storage,picking,transportation and distribution into an integrated service industry to enhance core competitiveness.At the same time,its own added value is also rising,which plays an extremely important role in shortening the circulation of goods and reducing the gap between production and sales.Therefore,a scientific and rational site selection can effectively reduce the operating costs of logistics and distribution systems.In real life,distribution center location is a complex system engineering.In the actual problems and options,the process is often dynamic,highly nonlinear,and the problems that arise from it are highly nonlinear complex.The problem is that the complexity of the solution is often exponential,and some traditional calculation methods have been difficult to solve.The classical particle swarm algorithm is widely used due to its simple model,low parameters,and easy implementation.Applications solve highly nonlinear optimization problems and various real-world engineering problems.In this paper,the particle swarm optimization algorithm is used to solve the problem of low convergence rate and slow convergence rate.The learning factor and position formula in particle swarm optimization algorithm are improved.The performance of the algorithm is verified by the test function.The accuracy of other algorithms is also improved.Comparing with the convergence curve and the solution results.It is proved that the improved particle swarm algorithm has faster convergence speed and stronger searching ability.Finally,the improved algorithm is used to solve the problem of distribution center location,which provides a new reference for intelligent optimization algorithm to solve the problem of location selection.It has certain theoretical and practical significance.