TY - GEN
T1 - Multi-armed Bandit-Based Metaheuristic Operator Selection
T2 - 6th International Conference on Optimization and Learning, OLA 2023
AU - Ábrego-Calderón, Pablo
AU - Crawford, Broderick
AU - Soto, Ricardo
AU - Rodriguez-Tello, Eduardo
AU - Cisternas-Caneo, Felipe
AU - Monfroy, Eric
AU - Giachetti, Giovanni
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Multi-armed bandit (MAB) is a well-known reinforcement learning algorithm that has shown outstanding performance for recommendation systems and other areas. On the other hand, metaheuristic algorithms have gained much popularity due to their great performance in solving complex problems with endless search spaces. Pendulum Search Algorithm (PSA) is a recently created metaheuristic inspired by the harmonic motion of a pendulum. Its main limitation is to solve combinatorial optimization problems, characterized by using variables in the discrete domain. To overcome this limitation, we propose to use a two-step binarization technique, which offers a large number of possible options that we call scheme. For this, we use MAB as an algorithm that learns and recommends a binarization schemes during the execution of the iterations (online). With the experiments carried out, we show that it delivers better results in solving the Set Covering problem than using a fixed binarization scheme.
AB - Multi-armed bandit (MAB) is a well-known reinforcement learning algorithm that has shown outstanding performance for recommendation systems and other areas. On the other hand, metaheuristic algorithms have gained much popularity due to their great performance in solving complex problems with endless search spaces. Pendulum Search Algorithm (PSA) is a recently created metaheuristic inspired by the harmonic motion of a pendulum. Its main limitation is to solve combinatorial optimization problems, characterized by using variables in the discrete domain. To overcome this limitation, we propose to use a two-step binarization technique, which offers a large number of possible options that we call scheme. For this, we use MAB as an algorithm that learns and recommends a binarization schemes during the execution of the iterations (online). With the experiments carried out, we show that it delivers better results in solving the Set Covering problem than using a fixed binarization scheme.
KW - Binarization Schemes
KW - Multi-Armed Bandit
KW - Pendulum Search Algorithm
KW - Reinforcement Learning
KW - Set Covering Problem
UR - http://www.scopus.com/inward/record.url?scp=85163313412&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-34020-8_19
DO - 10.1007/978-3-031-34020-8_19
M3 - Conference contribution
AN - SCOPUS:85163313412
SN - 9783031340192
T3 - Communications in Computer and Information Science
SP - 248
EP - 259
BT - Optimization and Learning - 6th International Conference, OLA 2023, Proceedings
A2 - Dorronsoro, Bernabé
A2 - Chicano, Francisco
A2 - Danoy, Gregoire
A2 - Talbi, El-Ghazali
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 3 May 2023 through 5 May 2023
ER -