面向物流车辆路径规划的自适应蚁群算法

Adaptive Ant Colony Algorithm for Logistics Vehicle Path Planning

针对蚁群算法在物流车辆路径规划易陷入局部最优,收敛速度较慢等问题,提出一种自适应蚁群算法。利用模拟退火算法构建蚁群算法的初始值,对蚁群算法的状态转移规则和信息素挥发因子进行自适应调整,结合2-opt邻域搜索算法改变解的质量并进行优选。使用VRP官网算例将改进前后的算法对比分析,表明改进后的算法具有更好的求解质量和收敛速度,可以有效解决物流车辆路径规划问题。

Aiming at the problem that the ant colony algorithm was easy to fall into local optimum and slow convergence speed in logistics vehicle routing planning, an adaptive ant colony algorithm was proposed. The simulated annealing algorithm was used to construct the initial value of the ant colony algorithm, and the state transition rule and pheromone volatilization factor of the ant colony algorithm were adaptively adjusted. The 2-opt neighborhood search algorithm was used to change the quality of the solution and optimize it. Examples of the VRP official website were used to compare and analyze the improved algorithm. Experiments show that the improved algorithm has better solution quality and convergence speed, which can effectively solve the logistics vehicle routing problem.