分层优化混合蚁群算法在车辆路径问题中的应用

Application of Hierarchical Optimization Hybrid Ant Colony Algorithm in Vehicle Routing Problem

蚁群算法在搜索过程中容易陷入局部最优,为了增强算法探索能力,将蚁群算法和邻域搜索策略相融合,提出一种分层优化的混合蚁群算法。算法分为初始求解和精度优化两个阶段,在初始求解阶段,主要采用蚁群算法寻找路径,结合邻域搜索策略进行扰动,增加解的多样性。在蚁群算法求出初始解后,算法进入精度优化阶段,对初始最优值进行深度邻域变换提高求解精度。采用车辆路径问题测试集验证算法性能,结果表明,混合蚁群算法在不同规模的车辆路径问题算例中都能获得较好的优化效果。

The ant colony algorithm tends to run into local optimum in the search process.In order to enhance the exploration ability of the algorithm,a hierarchical optimization hybrid ant colony algorithm is proposed by combining ant colony algorithm with neighborhood search strategy.The algorithm is divided into two stages:initial solution and precision optimization.In the initial solution stage,the ant colony algorithm is mainly used to find the path,and the neighborhood search strategy is combined to perturb,so as to increase the diversity of solutions.After the initial solution is obtained by the ant colony algorithm,the algorithm enters the precision optimization stage,and the depth neighborhood transformation is performed on the initial optimal value to improve the solution precision.The test set of vehicle routing problem is used to verify the performance of the algorithm.The results show that the hybrid ant colony algorithm can achieve better optimization results in different scale vehicle routing problem examples.