改进PSO算法在物流派送中的应用

Application of Improved PSO Algorithm in Logistics Delivery

粒子群算法由于其概念简单、参数较少、容易实现等优点,常被用于求解路径规划问题,在物流派送方面有着重要的应用.但其存在局部搜索能力差、易陷入局部极值、搜索精度低等缺陷,而遗传算法是从串集开始搜索的,具有很强的全局搜索能力.本文融合遗传算法中交叉变异的思想于粒子群算法中,提出了一种改进PSO算法,利用交叉操作筛选粒子初始位置,借助变异思想来提高最优解的搜索速率.仿真结果表明,该方法能够提高标准粒子群算法的搜索能力,获得了较好的收敛速度和最优路径.

Particle swarm optimization(PSO)is often used to solve path planning problems because of its simple concept,fewer parameters,and easy implementation,and it has important applications in logistics delivery.However,it has the defects of poor local search ability,easy to fall into local extremum,and low search accuracy.The genetic algorithm starts from the string set and has strong global search ability.In this paper,the idea of cross-variation in genetic algorithm is applied to the particle swarm optimization algorithm,and an improved PSO algorithm is proposed.The crossover operation is used to screen the initial position of the particle,and the variation idea is used to improve the search rate of the optimal solution.The simulation results show that the proposed method can improve the search ability of the standard particle swarm optimization algorithm and obtain better convergence speed and optimal path.