Study on Optimization of Logistics Distribution Routes Based on Opposition- based Learning Particle Swarm Optimization Algorithm

Abstract

In view of shortcomings of the particle swarm optimization algorithm such as poor late optimization ability and proneness to local optimization etc, this paper proposes an opposition-based learning particle swarm optimization (OBLPSO) algorithm for the optimization of logistics distribution routes, firstly, establishes a logistics distribution route optimization mathematical model, and then solves through collaboration and information exchange among particles, in- troduces an opposition-based learning mechanism to improve particle swarm optimization ability and convergence rate, and finally conducts simulation test on the performance of OBLPSO algorithm on Matlab 2012 platform. The simulation results show that OBLPSO algorithm can be used to obtain logistics distribution solutions with short time and rational routes and thus has certain practical value, compared with other logistics distribution route optimization algorithms.