A New Hybrid Differential Particle Swarm Optimization Algorithm and Application

Abstract

To solve the problem of low precision of B2C e-commerce logistics distribution optimization, a new hybrid differential particle swarm heuristic optimization algorithm is proposed to optimize B2C e-commerce logistics distribution. First of all, taking the particle swarm population as auxiliary mutation operator and the differential evolution algorithm for crossover operation, and to produce new offspring inherited the advantages of the father and mother generation, avoiding the single the algorithm of premature convergence and slow convergence speed of the problem. Compared with existing improved algorithm simulation, this algorithm can effectively jump out of local minima and prevent algorithm premature convergence speed quickly. Secondly, draw lessons from existing literature method for hybrid algorithm in B2C path optimization problem of engineering application has carried on the experimental study, through the simulation shows that the designed distribution scheme has faster computing speed and better target convergence value.