A Data-Driven Dynamic Discretization Framework to Solve Combinatorial Problems Using Continuous Metaheuristics

Felipe Cisternas-Caneo, BRODERICK CRAWFORD LABRIN, RICARDO JAVIER SOTO DE GIORGIS, HANNS ANIBAL DE LA FUENTE MELLA, Diego Tapia, José Lemus-Romani, Mauricio Castillo, Marcelo Becerra-Rozas, Fernando Paredes, Sanjay Misra

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

Combinatorial optimization problems are very common in the real world but difficult to solve. Among the promising algorithms that have been successful in solving these problems are metaheuristics. The two basic search behaviors used in metaheuristics are exploration and exploitation, and the success of metaheuristic search largely depends on the balance of these two behaviors. Machine learning techniques have provided considerable support to improve data-driven optimization algorithms. One of the techniques that stands out is Q-Learning, which is a reinforcement learning technique that penalizes or rewards actions according to the consequence it entails. In this work, a general discretization framework is proposed where Q-Learning can adapt a continuous metaheuristic to work in discrete domains. In particular, we use Q-learning so that the algorithm learns an optimal binarization schemEqe selection policy. The policy is dynamically updated based on the performance of the binarization schemes in each iteration. Preliminary experiments using our framework with sine cosine algorithm show that the proposal presents promising results compared to other algorithms.

Idioma originalInglés
Título de la publicación alojadaInnovations in Bio-Inspired Computing and Applications - Proceedings of the 11th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2020
EditoresAjith Abraham, Hideyasu Sasaki, Ricardo Rios, Niketa Gandhi, Umang Singh, Kun Ma
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas76-85
Número de páginas10
ISBN (versión impresa)9783030736026
DOI
EstadoPublicada - 2021
Publicado de forma externa
Evento11th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2020 and 10th World Congress on Information and Communication Technologies, WICT 2020 - Gunupur, India
Duración: 16 dic 202018 dic 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen1372 AISC
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365

Conferencia

Conferencia11th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2020 and 10th World Congress on Information and Communication Technologies, WICT 2020
País/TerritorioIndia
CiudadGunupur
Período16/12/2018/12/20

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