基于反向学习策略粒子群的物流配送路径优化研究

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

针对粒子群优化算法后期寻优能力,并易陷入局部最优等不足,提出了一种反向学习粒子群的物流配送路径优化算法(OBLPSO)。首先建立物流配送路径优化的数学模型,然后通过粒子之间的相互协作和信息交流进行求解,并引入反向学习机制提高粒子群寻优能力和收敛速度,最后在Matlab2012平台上对OBLPSO算法性能进行仿真测试。仿真结果表明,相对其它物流配送路径优化算法,OBLPSO算法可以获得时间短、路径合理的物流配送方案,具有一定的实用价值。

In this paper, in view of the shortcomings of the particle swarm optimization algorithm, we proposed an opposition- based learning particle swarm optimization algorithm（OBLPSO） for the optimization of logistics distribution routes, then introduced the specifics of the algorithm, and at the end, on the Matlab 2012 platform, conducted a simulation test on the algorithm to find that the algorithm was superior to other optimization algorithms in obtaining logistics distribution solutions.