基于动态聚类混合拓扑结构粒子群算法的PDVRPTF

经典物流配送模型的目标、约束条件不够全面，在实际应用中存在一定缺陷，对此，构建了时间窗和油耗取送一体化的物流配送路径优化模型（PDVRPTF）.设计了一种基于k-medoids动态聚类混合拓扑结构粒子群算法，解决了经典粒子群算法在求解此类模型时容易陷入局部最优解的问题.仿真结果表明，改进型粒子群算法能很好地跳出局部最优解，并快速收敛于全局最优解，且该算法可有效求解物流配送路径优化的问题.

PDVRPTF Based on Dynamic Clustering Hybrid Topological Structure Particle Swarm Optimization

Aiming at the problem that the classic logistics distribution model considers the target, the constraints are not comprehensive enough and there are certain defects in the practical application, a integrated pickup and distribution vehicle routing problem on the basis of the classical model considering time window and fuel consumption (PDVRPTF) is constructed. Hybrid topological structure of particle swarm optimization based on k-medoids dynamic clustering is designed, which solves the problem that classical particle swarm optimization is easy to fall into local optimal solution when solving such models. The simulation results show that the improved particle swarm optimization can jump out of the local optimal solution quickly and converge to the global optimal solution quickly, which solve the logistics distribution path optimization problem effectively.