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
Published in:Mathematics (Basel) 2021-08, Vol.9 (16), p.1839
Main Authors: Crawford, Broderick, Soto, Ricardo, Lemus-Romani, José, Becerra-Rozas, Marcelo, Lanza-Gutiérrez, José, Caballé, Nuria, Castillo, Mauricio, Tapia, Diego, Cisternas-Caneo, Felipe, García, José, Astorga, Gino, Castro, Carlos, Rubio, José-Miguel
Format: Article
Language:English
Subjects:
Algorithms
balanced metaheuristics
Combinatorial analysis
Decision making
Design
Exploitation
Heuristic
Heuristic methods
Knapsack problem
Machine learning
Mathematical programming
Mathematics
metaheuristics
Mutation
Operators
Optimization
Q-Learning
Sine-Cosine Algorithm
Traveling salesman problem
Trigonometric functions
Whale Optimization Algorithm
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math9161839