TY - JOUR
T1 - Q-learnheuristics
T2 - Towards data-driven balanced metaheuristics
AU - Crawford, Broderick
AU - Soto, Ricardo
AU - Lemus-Romani, José
AU - Becerra-Rozas, Marcelo
AU - Lanza-Gutiérrez, José M.
AU - Caballé, Nuria
AU - Castillo, Mauricio
AU - Tapia, Diego
AU - Cisternas-Caneo, Felipe
AU - García, José
AU - Astorga, Gino
AU - Castro, Carlos
AU - Rubio, José Miguel
N1 - Funding Information:
Acknowledgments: Broderick Crawford is supported by Grant ANID/FONDECYT/REGULAR/ 1210810: “DATA-DRIVEN AMBIDEXTROUS METAHEURISTICS: USING MACHINE LEARNING APPROACHES TO MANAGE BALANCE OF EXPLORATION AND EXPLOITATION WHEN SOLVING COMBINATORIAL PROBLEMS WITH CONTINUOUS SWARM INTELLIGENCE ALGORITHMS”. Ricardo Soto is supported by Grant ANID/FONDECYT/REGULAR/1190129: “BUILDING REACTIVE LEARNING-BASED HYBRID METAHEURISTICS”. José Lemus-Romani is supported by National Agency for Research and Development (ANID)/Scholarship Program/ DOCTORADO NACIONAL/2019-21191692. Marcelo Becerra-Rozas is supported by National Agency for Research and Development (ANID)/Scholarship Program/DOCTORADO NACIONAL/ 2021-21210740. José García was supported by the Grant ANID/FONDECYT/INICIACION/ 11180056: “APPLYING MACHINE LEARNING TECHNIQUES TO METAHEURISTIC ALGORITHMS TO SOLVE COMBINATORIAL OPTIMIZATION PROBLEMS”.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/8/2
Y1 - 2021/8/2
N2 - 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 explorationexploitation 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.
AB - 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 explorationexploitation 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.
KW - Balanced metaheuristics
KW - Metaheuristics
KW - Q-Learning
KW - Sine-Cosine Algorithm
KW - Whale Optimization Algorithm
UR - http://www.scopus.com/inward/record.url?scp=85113773476&partnerID=8YFLogxK
U2 - 10.3390/math9161839
DO - 10.3390/math9161839
M3 - Article
AN - SCOPUS:85113773476
SN - 2227-7390
VL - 9
JO - Mathematics
JF - Mathematics
IS - 16
M1 - 1839
ER -