Logistics Route Optimization Based on Improved Particle Swarm Optimization

Abstract

Abstract An improved particle swarm optimization (PSO) algorithm is presented by dynamically adjusting the inertia weight in the iterative process of PSO, and it is used to solve the problem of logistics route optimization. This algorithm can not only improve the convergence speed, but also avoid falling into local optimum. In the process of improving the standard algorithm, two methods are proposed to adjust the inertia weight value according to the number of iterations. One is piecewise linear decreasing, another is linear decreasing. The results show that linear decline is better than piecewise linear decline to achieve the purpose of optimization, which is more conducive to accelerate the convergence rate and enhance the ability of optimization. Through the simulation experiment of the specific vehicle routing optimization problem, the results show that after the improvement, the optimization performance is enhanced, the optimization speed is accelerated, and the complexity is not increased, which greatly improves the performance of the original algorithm.