基于改进蚁群算法的CVRP问题研究

车辆路径优化问题归属于NP-hard问题;针对基本蚁群算法求解效率低下,可行解质量不高,容易陷入局部最优解的情况,在充分考虑具有一般性的车辆路径优化问题的数学模型与解决方案后,提出了一种带有轮盘赌运算与2-opt优化运算相结合的改进蚁群算法,算法在运算过程中对选取路径的概率进行二次计算,扩大了全局的搜索范围;同时对得到的路径进行内部优化,增强了局部搜索能力,提高了解的质量;通过MATLAB软件进行仿真实验的结果表明:相较于基本的ACO算法以及遗传算法得到的结果,改进的蚁群算法在性能上和求解的质量具有很大的优势,可以更好地解决带有容量约束的车辆路径优化问题,为相应的企业更好地节省物流成本。

Research on CVRP Based on Improved Ant Colony Algorithm

Vehicle routing optimization problem belongs to NP-hard problem. In view of the low efficiency of the basic ant colony algorithm, the low quality of feasible solutions, and the tendency to fall into local optimal solutions, after fully considering the mathematical model and solution of the general vehicle routing problem, an improved ant colony algorithm with roulette operation and 2-opt optimization operation is proposed. The algorithm performs a second calculation on the probability of the selected path during the operation process, which expands the global search range; at the same time, the obtained path is internally optimized to enhance improved local search capabilities and improve the quality of the solution. The results of simulation experiments through MATLAB software show that compared with the results obtained by the basic ACO algorithm and genetic algorithm, the improved ant colony algorithm has great advantages in performance and solution quality. It can better solve the vehicle routing problem with capacity constraints for the corresponding enterprises to better save logistics costs.