基于FA-IACS算法的车辆路径问题优化

针对传统蚁群系统算法在解决有容量约束的普适性车辆路径优化中易陷入局部最优和收敛速度慢等问题,提出了一种改进的蚁群系统算法.采用改进的距离启发函数因子调整蚂蚁状态转移概率,利用改进编码方式的萤火虫算法作为搜索机制,改善蚁群系统的全局搜索能力,应用信息素震荡程序探索新路径的信息素,避免陷入局部最优.结果表明,该算法提高了全局搜索能力,能够节约寻找最优路径的时间,加快收敛速度,具有更好的鲁棒性.

Optimization of vehicle routing problem based on FA-IACS algorithm

Aiming at the problem that the traditional ant colony system algorithm is easy to fall into local optimum and slow to converge in the optimization of general vehicle routing with capacity constraints, an improved ant colony system algorithm was proposed. The state transition probability of ants was adjusted with an improved distance heuristic function factor. A firefly search with improved coding modes was introduced as search mechanism to improve the global search ability of ant colony system. The pheromone oscillation program was applied to explore the pheromone of new routes and avoid falling into local optimum. The results show that the as-proposed algorithm can improve the global search ability, save the time for finding the optimal route, speed up the convergence speed, and exhibit better robustness.