元启发式
迭代局部搜索
车辆路径问题
数学优化
启发式
迭代函数
局部搜索(优化)
计算机科学
布线(电子设计自动化)
数学
计算机网络
数学分析
作者
Vinícius R. Máximo,Jean‐François Cordeau,Mariá C. V. Nascimento
摘要
Abstract Adaptive iterated local search (AILS) is a recently proposed metaheuristic paradigm that focuses on adapting the diversity control of iterated local search by online learning mechanisms. It has been successfully applied to the capacitated vehicle routing problem (CVRP) and the heterogeneous vehicle routing problem. Hybridizing it with path relinking (PR) has further improved the intensification of the method for the CVRP, providing outstanding results. However, the potential of this metaheuristic has not yet been investigated on other combinatorial optimization problems, such as location problems. In this paper, we develop a version of AILS for the maximal covering location problem (MCLP). This problem consists of locating a number of facilities to maximize the covered customer demand, where a given facility location can meet the demand of customers located within a coverage radius. Experiments on large‐scale instances of the MCLP indicate that AILS hybridized with PR, called AILS‐PR, outperforms the state‐of‐the‐art metaheuristic.
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