求解带硬时间窗车辆路径问题的自适应蚁群算法

ADAPTIVE ANT COLONY OPTIMIZATION ALGORITHM FOR SOLVING VEHICLE ROUTING PROBLEM WITH HARD TIME WINDOWS

蚁群算法具有较强的鲁棒性和优良的分布式计算机制。研究重点是对现有的求解带硬时间窗的车辆路径问题VRP-H (Vehicle Routing Problem with Hard Time Windows)的蚁群算法作出更好的改进,使得算法的计算效率更高且得到的解更优,提出了蚁群算法的改进算法-改进的自适应蚁群算法。该算法先用自适应蚁群算法对VRP-H求得一个可行解,再利用多种改善方法对初始解进一步优化,从而得到最优解。测试时选用Solomon提出的题库,结果表明该算法能够有效地求解VRP-H。

Ant Colony Optimization （ACO） algorithm has stronger robustness and a good distributed computer system. In this paper the focus of the study is on better improving the current ACO algorithm which solves Vehicle Routing Problem with Hard Time Windows（VRP-H） ,to make the algorithm be more effective in computation as well as achieve more optimal solution. The modified algorithm of ACO algorithm was put forward-Improved Adaptive ACO algorithm. The algorithm was that to seek a feasible solution for solving VRP-H by adaptive ACO algorithm first, then to use a variety of methods to further optimize and gain optimal solution. Experimental tests were selected from the Solomon＇ s test database and the numerical results showed that the algorithm can effectively solve VRP-H.