Examinando por Autor "Casado, Alejandra"

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A GRASP algorithm with Tabu Search improvement for solving the maximum intersection of k-subsets problem
(Springer, 2022) Casado, Alejandra; Pérez-Peló, Sergio; Sánchez-Oro, Jesús; Duarte, Abraham
The selection of individuals with similar characteristics from a given population have always been a matter of interest in several scientific areas: data privacy, genetics, art, among others. This work is focused on the maximum intersection of k-subsets problem (kMIS). This problem tries to find a subset of k individuals with the maximum number of features in common from a given population and a set of relevant features. The research presents a Greedy Randomized Adaptive Search Procedure (GRASP) where the local improvement is replaced by a complete Tabu Search metaheuristic with the aim of further improving the quality of the obtained solutions. Additionally, a novel representation of the solution is considered to reduce the computational effort. The experimental comparison carefully analyzes the contribution of each part of the algorithm to the final results as well as performs a thorough comparison with the state-of-the-art method. Results, supported by non-parametric statistical tests, confirms the superiority of the proposal.
Variable neighborhood search approach with intensified shake for monitor placement
(Wiley, 2022) Casado, Alejandra; Mladenovíc, Nenad; Sánchez-Oro, Jesús; Duarte, Abraham
Several problems are emerging in the context of communication networks and mostof them must be solved in reduced computing time since they affect to critical tasks.In this research, the monitor placement problem is tackled. This problem tries tocover the communications of an entire network by locating a monitor in specificnodes of the network, in such a way that every link remains surveyed. In case thata solution cannot be generated in the allowed computing time, a penalty will beassumed for each link uncovered. The problem is addressed by considering the vari-able neighborhood search framework, proposing a novel constructive method, anintelligent local search to optimize the improvement phase, and an intensified shaketo guide the search to more promising solutions. The proposed algorithm is com-pared with a hybrid search evolutionary algorithm over a set of instances derivedfrom real-life networks to prove its performance.