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.

元启发式算法被广泛用于解决硬优化问题，如车辆路径问题 (VRP)，对于这些问题，精确求解方法是不切实际的。特别是，基于局部搜索的元启发式算法已成功应用于有能力的 VRP (CVRP)。CVRP 旨在为一组给定的相同车辆定义最低成本的交付路线，因为每辆车只行驶一条路线并且有一个（中央）仓库。CVRP 的最佳元启发式方法通过将特定的爬山机制（例如多样化策略）嵌入到解决方案方法中来避免陷入局部最优。本文介绍了一种新型自适应版本的迭代局部搜索与路径重新链接 (AILS-PR) 与 CVRP 的混合。本文的主要贡献是一种自动机制来控制元启发式的多样性步骤，使其摆脱局部最优。100 个基准 CVPR 实例的实验结果表明，AILS-PR 优于最先进的 CVRP 元启发式算法。