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Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

Author

Listed:
  • Broderick Crawford

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile)

  • Ricardo Soto

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile)

  • José Lemus-Romani

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile)

  • Marcelo Becerra-Rozas

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile)

  • José M. Lanza-Gutiérrez

    (Departamento de Ciencias de la Computación, Escuela Politécnica Superior, Universidad de Alcalá, 28805 Alcalá de Henares, Spain)

  • Nuria Caballé

    (Departamento de Física y Matemáticas, Facultad de Ciencias, Universidad de Alcalá, 28802 Alcalá de Henares, Spain)

  • Mauricio Castillo

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile)

  • Diego Tapia

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile)

  • Felipe Cisternas-Caneo

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile)

  • José García

    (Escuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile)

  • Gino Astorga

    (Escuela de Negocios Internacionales, Universidad de Valparaíso, Alcalde Prieto Nieto 452, Viña del Mar 2572048, Chile)

  • Carlos Castro

    (Departamento de Informática, Universidad Técnica Federico Santa María, Avenida España 1680, Valparaíso 2390123, Chile)

  • José-Miguel Rubio

    (Escuela de Computación e Informática, Universidad Bernardo O’Higgins, Av. Viel 1497, Santiago 8370993, Chile)

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.

Suggested Citation

  • 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 , 2021. "Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1839-:d:608235
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    References listed on IDEAS

    as
    1. Fred Glover & Michele Samorani, 2019. "Intensification, Diversification and Learning in metaheuristic optimization," Journal of Heuristics, Springer, vol. 25(4), pages 517-520, October.
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