TY - GEN
T1 - Embedding Q-Learning in the selection of metaheuristic operators
T2 - 2021 IEEE International Conference on Automation/24th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2021
AU - Tapia, Diego
AU - Crawford, Broderick
AU - Soto, Ricardo
AU - Palma, Wenceslao
AU - Lemus-Romani, Jose
AU - Cisternas-Caneo, Felipe
AU - Castillo, Mauricio
AU - Becerra-Rozas, Marcelo
AU - Paredes, Fernando
AU - Misra, Sanjay
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/3/22
Y1 - 2021/3/22
N2 - In the different situations present in the industry, combinatorial problems are increasingly frequent. This paper presents the interaction of Metaheuristics and Machine Learning, specifically as Machine Learning can be a support to enhance Metaheuristics. The resolution of the Set Covering Problem is presented, using the Grey Wolf Optimizer and Sine Cosine Algorithm metaheuristics that have been improved by adding a Q-Learning technique for the selection of a Discretization Scheme, using two-steps, intelligently choosing which transfer function to use and which binarization technique to apply in each iteration. The results show a better result for the Grey Wolf Optimizer with Q-Learning configuration, compared to other configurations in the literature, obtaining a better balance between exploration and exploitation.
AB - In the different situations present in the industry, combinatorial problems are increasingly frequent. This paper presents the interaction of Metaheuristics and Machine Learning, specifically as Machine Learning can be a support to enhance Metaheuristics. The resolution of the Set Covering Problem is presented, using the Grey Wolf Optimizer and Sine Cosine Algorithm metaheuristics that have been improved by adding a Q-Learning technique for the selection of a Discretization Scheme, using two-steps, intelligently choosing which transfer function to use and which binarization technique to apply in each iteration. The results show a better result for the Grey Wolf Optimizer with Q-Learning configuration, compared to other configurations in the literature, obtaining a better balance between exploration and exploitation.
KW - Combinatorial Optimization
KW - Grey wolf optimization
KW - Metaheuristics
KW - Q-Learning
KW - Sine Cosine Algorithm
UR - http://www.scopus.com/inward/record.url?scp=85114209852&partnerID=8YFLogxK
U2 - 10.1109/ICAACCA51523.2021.9465259
DO - 10.1109/ICAACCA51523.2021.9465259
M3 - Conference contribution
AN - SCOPUS:85114209852
T3 - 2021 IEEE International Conference on Automation/24th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2021
BT - 2021 IEEE International Conference on Automation/24th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 March 2021 through 26 March 2021
ER -