论文标题
混合自适应迭代的本地搜索，并对电容的车辆路由问题多样化控制
A hybrid adaptive Iterated Local Search with diversification control to the Capacitated Vehicle Routing Problem
论文作者
论文摘要
元硫疗法被广泛用于解决硬性优化问题，例如车辆路由问题（VRP），因此，精确的解决方案方法是不切实际的。特别是，基于本地搜索的元启发术已成功地应用于电容的VRP（CVRP）。 CVRP的目的是定义给定的一组相同车辆的最低成本交付路线，因为每辆车仅行驶一条路线，并且有一个（中央）仓库。 CVRP的最佳元启发式学避免将特定的爬山机制（例如多元化策略）嵌入到解决方案方法中，从而避免陷入本地Optima。本文将新型自适应版本的迭代局部搜索与路径链接（AILS-pr）介绍给CVRP。本文的主要贡献是一种自动机制，可以控制元启发式的多样性步骤，以使其从当地的Optima中逃脱。使用100个基准CVPR实例实验的结果表明，AILS-PR的表现优于最先进的CVRP元启发术。
Metaheuristics are widely employed to solve hard optimization problems, like vehicle routing problems (VRP), for which exact solution methods are impractical. In particular, local search-based metaheuristics have been successfully applied to the capacitated VRP (CVRP). The CVRP aims at defining the minimum-cost delivery routes for a given set of identical vehicles since each vehicle only travels one route and there is a single (central) depot. The best metaheuristics to the CVRP avoid getting stuck in local optima by embedding specific hill-climbing mechanisms such as diversification strategies into the solution methods. This paper introduces a hybridization of a novel adaptive version of Iterated Local Search with Path-Relinking (AILS-PR) to the CVRP. The major contribution of this paper is an automatic mechanism to control the diversity step of the metaheuristic to allow it to escape from local optima. The results of experiments with 100 benchmark CVPR instances show that AILS-PR outperformed the state-of-the-art CVRP metaheuristics.
