Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics
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 pr...
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/9/16/1839 |
| _version_ | 1797523060695236608 |
|---|---|
| author | Broderick Crawford Ricardo Soto José Lemus-Romani Marcelo Becerra-Rozas José M. Lanza-Gutiérrez Nuria Caballé Mauricio Castillo Diego Tapia Felipe Cisternas-Caneo José García Gino Astorga Carlos Castro José-Miguel Rubio |
| author_facet | Broderick Crawford Ricardo Soto José Lemus-Romani Marcelo Becerra-Rozas José M. Lanza-Gutiérrez Nuria Caballé Mauricio Castillo Diego Tapia Felipe Cisternas-Caneo José García Gino Astorga Carlos Castro José-Miguel Rubio |
| author_sort | Broderick Crawford |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:38:05Z |
| format | Article |
| id | doaj.art-6ce944acc1a842a38ea87a47e5843025 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T08:38:05Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-6ce944acc1a842a38ea87a47e58430252023-11-22T08:32:42ZengMDPI AGMathematics2227-73902021-08-01916183910.3390/math9161839Q-Learnheuristics: Towards Data-Driven Balanced MetaheuristicsBroderick Crawford0Ricardo Soto1José Lemus-Romani2Marcelo Becerra-Rozas3José M. Lanza-Gutiérrez4Nuria Caballé5Mauricio Castillo6Diego Tapia7Felipe Cisternas-Caneo8José García9Gino Astorga10Carlos Castro11José-Miguel Rubio12Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, ChileDepartamento de Ciencias de la Computación, Escuela Politécnica Superior, Universidad de Alcalá, 28805 Alcalá de Henares, SpainDepartamento de Física y Matemáticas, Facultad de Ciencias, Universidad de Alcalá, 28802 Alcalá de Henares, SpainEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, ChileEscuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, ChileEscuela de Negocios Internacionales, Universidad de Valparaíso, Alcalde Prieto Nieto 452, Viña del Mar 2572048, ChileDepartamento de Informática, Universidad Técnica Federico Santa María, Avenida España 1680, Valparaíso 2390123, ChileEscuela de Computación e Informática, Universidad Bernardo O’Higgins, Av. Viel 1497, Santiago 8370993, ChileOne 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.https://www.mdpi.com/2227-7390/9/16/1839metaheuristicsbalanced metaheuristicsQ-LearningWhale Optimization AlgorithmSine-Cosine Algorithm |
| spellingShingle | Broderick Crawford Ricardo Soto José Lemus-Romani Marcelo Becerra-Rozas José M. Lanza-Gutiérrez Nuria Caballé Mauricio Castillo Diego Tapia Felipe Cisternas-Caneo José García Gino Astorga Carlos Castro José-Miguel Rubio Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics Mathematics metaheuristics balanced metaheuristics Q-Learning Whale Optimization Algorithm Sine-Cosine Algorithm |
| title | Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics |
| title_full | Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics |
| title_fullStr | Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics |
| title_full_unstemmed | Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics |
| title_short | Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics |
| title_sort | q learnheuristics towards data driven balanced metaheuristics |
| topic | metaheuristics balanced metaheuristics Q-Learning Whale Optimization Algorithm Sine-Cosine Algorithm |
| url | https://www.mdpi.com/2227-7390/9/16/1839 |
| work_keys_str_mv | AT broderickcrawford qlearnheuristicstowardsdatadrivenbalancedmetaheuristics AT ricardosoto qlearnheuristicstowardsdatadrivenbalancedmetaheuristics AT joselemusromani qlearnheuristicstowardsdatadrivenbalancedmetaheuristics AT marcelobecerrarozas qlearnheuristicstowardsdatadrivenbalancedmetaheuristics AT josemlanzagutierrez qlearnheuristicstowardsdatadrivenbalancedmetaheuristics AT nuriacaballe qlearnheuristicstowardsdatadrivenbalancedmetaheuristics AT mauriciocastillo qlearnheuristicstowardsdatadrivenbalancedmetaheuristics AT diegotapia qlearnheuristicstowardsdatadrivenbalancedmetaheuristics AT felipecisternascaneo qlearnheuristicstowardsdatadrivenbalancedmetaheuristics AT josegarcia qlearnheuristicstowardsdatadrivenbalancedmetaheuristics AT ginoastorga qlearnheuristicstowardsdatadrivenbalancedmetaheuristics AT carloscastro qlearnheuristicstowardsdatadrivenbalancedmetaheuristics AT josemiguelrubio qlearnheuristicstowardsdatadrivenbalancedmetaheuristics |