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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. [ABSTRACT FROM AUTHOR]

A recent study on the classical capacitated vehicle routing problem (CVRP) introduced an adaptive version of the widely used iterated local search paradigm, hybridized with a path-relinking (PR) strategy. The solution method, called adaptive iterated local search (AILS)-PR, outperformed existing meta-heuristics for the CVRP on benchmark instances. However, tests on large-scale instances suggest that PR is too slow, making AILS-PR less advantageous in this case. To overcome this challenge, this paper presents an AILS combined with mechanisms to handle large CVRP instances, called AILS-II. The computational cost of this implementation is reduced, whereas the algorithm also searches the solution space more efficiently. AILS-II is very competitive on smaller instances, outperforming the other methods from the literature with respect to the average gap to the best-known solutions. Moreover, AILS-II consistently outperforms the state of the art on larger instances with up to 30,000 vertices. History: Accepted by Ted Ralphs, Area Editor for Software Tools. This paper has been accepted for the INFORMS Journal on Computing Special Issue on Software Tools for Vehicle Routing. Funding: This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo [Grants 2013/07375-0, 2019/22067-6, and 2022/05803-3] and the Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grants 309385/2021-0 and 403735/2021-1]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0106) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0106). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Hybrid heuristic methods have lately been pointed out as an efficient approach to combinatorial optimization problems. The main reason behind this is that, by combining components from different metaheuristics, it is possible to explore solutions (which would be unreachable without hybridization) in the search space. In particular, evolutionary algorithms may get trapped into local optimum solutions due to the insufficient diversity of the solutions influencing the search process. This paper presents a hybridization of the recently proposed metaheuristic—intelligent-guided adaptive search (IGAS)—with the well-known path-relinking algorithm to solve large scale instances of the maximum covering location problem. Moreover, it proposes a slight change in IGAS that was tested through computational experiments and has shown improvement in its computational cost. Computational experiments also attested that the hybridized IGAS outperforms the results found in the literature. [ABSTRACT FROM AUTHOR]