TY - GEN
T1 - An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model
AU - Castillo, Mauricio
AU - CRAWFORD LABRIN, BRODERICK
AU - SOTO DE GIORGIS, RICARDO JAVIER
AU - PALMA MUÑOZ, WENCESLAO ENRIQUE
AU - Lemus-Romani, José
AU - Tapia, Diego
AU - Cisternas-Caneo, Felipe
AU - Becerra-Rozas, Marcelo
AU - Paredes, Fernando
AU - Misra, Sanjay
N1 - Funding Information:
Felipe Cisternas-Caneo and Marcelo Becerra-Rozas are supported by Grant DI Investigaci?n Interdisciplinaria del Pregrado/VRIEA/PUCV/ 039.324/2020. Broderick Crawford is supported by Grant CONICYT/FONDECYT/ REGULAR /1171243. Ricardo Soto is supported by Grant CONICYT/FONDECYT/ REGULAR /1190129. Jos? Lemus-Romani is supported by National Agency for Research and Development (ANID)/Scholarship Program/DOCTORADO NACIONAL/2019-21191692.
Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - In this work we implemented a version of the Galactic Swarm Optimization metaheuristic algorithm tuned by a hidden Markov model. The Galactic Swarm Optimization algorithm is an abstraction of the motion of stars within galaxies on the first level, and galaxies within a cluster of galaxies on the second level. We address the problem of controlling the metaheuristic parameters by identifying the state of the algorithm at each iteration i, using the Hidden Markov Model framework and updating the Galactic Swarm Optimization parameters accordingly. The results obtained show an improvement compared to the original algorithm using the fixed parameters found in the literature. In addition, the results are compared against other algorithms that use different techniques and hybridizations to solve the same problem, showing an improvement in performance with a similar quality for the solutions obtained.
AB - In this work we implemented a version of the Galactic Swarm Optimization metaheuristic algorithm tuned by a hidden Markov model. The Galactic Swarm Optimization algorithm is an abstraction of the motion of stars within galaxies on the first level, and galaxies within a cluster of galaxies on the second level. We address the problem of controlling the metaheuristic parameters by identifying the state of the algorithm at each iteration i, using the Hidden Markov Model framework and updating the Galactic Swarm Optimization parameters accordingly. The results obtained show an improvement compared to the original algorithm using the fixed parameters found in the literature. In addition, the results are compared against other algorithms that use different techniques and hybridizations to solve the same problem, showing an improvement in performance with a similar quality for the solutions obtained.
KW - Galactic Swarm Optimization
KW - Hidden Markov model
KW - Machine learning
KW - Metaheuristics
KW - Parameter control
UR - http://www.scopus.com/inward/record.url?scp=85105866292&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-73689-7_34
DO - 10.1007/978-3-030-73689-7_34
M3 - Conference contribution
AN - SCOPUS:85105866292
SN - 9783030736880
T3 - Advances in Intelligent Systems and Computing
SP - 354
EP - 363
BT - Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020
A2 - Abraham, Ajith
A2 - Ohsawa, Yukio
A2 - Gandhi, Niketa
A2 - Jabbar, M. A.
A2 - Haqiq, Abdelkrim
A2 - McLoone, Seán
A2 - Issac, Biju
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 15 December 2020 through 18 December 2020
ER -