Q-Learnheuristics: towards data-driven balanced metaheuristics
Authors
Crawford Labrin, Broderick; Soto, Ricardo; Lemun Romani, José; Becerra Rozas, Marcelo; Lanza Gutiérrez, José Manuel; [et al.]Identifiers
Permanent link (URI): http://hdl.handle.net/10017/60418DOI: 10.3390/math9161839
ISSN: 2227-7390
Publisher
MDPI
Date
2021-08-04Affiliation
Universidad de Alcalá. Departamento de Ciencias de la Computación; Universidad de Alcalá. Departamento de Física y Matemáticas. Unidad docente MatemáticasBibliographic citation
Crawford, B. [et al.], 2021, "Q-Learnheuristics: towards data-driven balanced metaheuristics", Mathematics, vol. 9, no. 16, pp. 1-26.
Keywords
Metaheuristics
Balanced metaheuristics
Q-Learning
Whale Optimization Algorithm
Sine-Cosine Algorithm
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.3390/math9161839Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2021 The Authors
Access rights
info:eu-repo/semantics/openAccess
Abstract
One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.
Files in this item
Files | Size | Format |
|
---|---|---|---|
Q-Learnheuristics_Crawford_Mat ... | 1.385Mb |
|
Files | Size | Format |
|
---|---|---|---|
Q-Learnheuristics_Crawford_Mat ... | 1.385Mb |
|
Collections
- CCOMPUT - Artículos [86]