Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

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...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Mathematics
Subjects:
metaheuristics
balanced metaheuristics
Q-Learning
Whale Optimization Algorithm
Sine-Cosine Algorithm
Online Access:https://www.mdpi.com/2227-7390/9/16/1839

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https://www.mdpi.com/2227-7390/9/16/1839

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