Examinando por Autor "Duarte, A."

Mostrando 1 - 3 de 3

A novel parallel framework for scatter search
(Elsevier, 2025-04-08) Casado, A.; Pérez-Peló, S.; Sánchez-Oro , J.; Duarte, A.; Laguna, M.
Scatter search (SS) is a well-established metaheuristic for hard combinatorial optimization problems. SS is characterized by its versatility and ease of context adaptation and implementation. Although the literature includes SS parallelization schemes for specific problems, a general parallel framework for scatter search has not been developed and tested. We introduce three SS parallel designs, each focusing on a different task, namely, reducing computational time, increasing search exploration, and balancing search intensification and diversification. The proposed designs are tested on problems where the state of the art is a traditional (sequential) SS approach. This testing platform helps us assess the contributions of the parallel computing strategies to solution speed and quality. Our publicly available code is designed to be adapted to optimization problems that are not considered here. The results show promising avenues for establishing a general framework of SS parallelization.
A variable neighborhood search for the median location problem with interconnected facilities
(Wiley, 2024-04-24) Lozano-Osorio, I.; Sánchez-Oro, J.; López-Sánchez, A.D.; Duarte, A.
The p-median problem has been widely studied in the literature. However, there are several variants that include new constraints to the classical problem that make it more realistic. In this work, we study the variant that considers interconnected facilities, that is, the distances between each pair of facilities are less than or equal to a certain threshold . This optimization problem, usually known as the median location problem with interconnected facilities, consists of locating a set of interconnected facilities to minimize the distance between those interconnected facilities and the demand points. The large variety of real-world applications that fit into this model makes it attractive to design an algorithm able to solve the problem efficiently. To this end, a procedure based on the variable neighborhood search methodology is designed and implemented by using problem-dependent neighborhoods. Experimental results show that our proposal is able to reach most of the optimal solutions when they are known. Additionally, it outperforms previous state-of-the-art methods in those instances where the optima are unknown. These results are further confirmed by conducting nonparametrical statistical tests
Solving the edge-disjoint paths problem using a two-stage method
(Wiley, 2020) Martin, B.; Sanchez, A.; Beltrán-Royo, César; Duarte, A.
There exists a wide variety of network problems where several connection requests occur simultaneously. In general, each request is attended by finding a route in the network, where the origin and destination of such a route are those hosts that wish to establish a connection for information exchange. As is well documented in the related literature, the exchange of information through disjoint routes increases the effective bandwidth, velocity, and the probability of receiving the corresponding information. The definition of disjoint paths may refer to nodes, edges, or both. One of the most studied variants is the one where disjointness implies not to share edges. This optimization problem is usually known as the maximum edge-disjoint paths problem. This urn:x-wiley:09696016:media:itor12544:itor12544-math-0001-hard optimization problem has applications in real-time communications, very large scale integration design, scheduling, bin packing, or load balancing. The proposed approach hybridizes an integer linear programming formulation of the problem with an evolutionary algorithm. Empirical results using 174 previously reported instances show that the proposed procedure compares favorably to previous metaheuristics for this problem. We confirm the significance of the results by conducting nonparametric statistical tests