基于混沌扰动和邻域交换的蚁群算法求解车辆路径问题

为求解车辆路径问题，提出一种新的基于混沌扰动和邻域交换的蚁群算法。针对标准蚁群算法存在搜索时间长，容易出现早熟收敛，得到的解不是最优解等缺点，新算法利用混沌的随机性、遍历性及规律性，在算法陷入早熟时，对小部分路径的信息素采用混沌扰动策略进行调整；针对标准蚁群算法的贪心规则随机性缺点，新算法采用邻域交换策略对最优解进行调整。在用于求解不同规模车辆路径问题的仿真结果表明,新算法比标准蚁群算法和遗传算法具有更好的效果。

Ant colony optimization algorithm based on chaotic disturbance and neighborhood exchange for vehicle routing problem

A new ant colony algorithm based on chaotic disturbance and neighborhood exchange was proposed to solve the Vehicle Routing Problem(VRP).Concerning the standard ant colony algorithm’s shortcomings such as long search time,being prone to premature convergence,non-optimal solution and so on,the new algorithm used the randomness,ergodicity and regularity of chaos to adjust the pheromone of a small part of the routes with the chaotic disturbance strategy when the algorithm was getting into a prematurity.For the standard ant colony algorithm has the greedy rule with randomness,the new algorithm used the neighborhood exchange strategy to adjust the optimal solution.The simulation results show that the new algorithm is better than the standard ant colony algorithm and genetic algorithm when solving the VRPs of different sizes.