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An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model

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Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020) (SoCPaR 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1383))

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Abstract

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.

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Acknowledgements

Felipe Cisternas-Caneo and Marcelo Becerra-Rozas are supported by Grant DI Investigación 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. José Lemus-Romani is supported by National Agency for Research and Development (ANID)/Scholarship Program/DOCTORADO NACIONAL/2019-21191692.

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Authors and Affiliations

  1. Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Mauricio Castillo, Broderick Crawford, Ricardo Soto, Wenceslao Palma, José Lemus-Romani, Diego Tapia, Felipe Cisternas-Caneo & Marcelo Becerra-Rozas

  2. Universidad Diego Portales, Santiago, Chile

    Fernando Paredes

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

Authors
  1. Mauricio Castillo
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  2. Broderick Crawford
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  3. Ricardo Soto
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  4. Wenceslao Palma
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  5. José Lemus-Romani
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  6. Diego Tapia
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  7. Felipe Cisternas-Caneo
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  8. Marcelo Becerra-Rozas
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  9. Fernando Paredes
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  10. Sanjay Misra
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Corresponding author

Correspondence to Mauricio Castillo .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Systems Innovation, School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan

    Yukio Ohsawa

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  4. Vardhaman College of Engineering, Hyderabad, Telangana, India

    M.A. Jabbar

  5. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  6. Queen’s University, Belfast, UK

    Seán McLoone

  7. Northumbria University, Newcastle, UK

    Biju Issac

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Castillo, M. et al. (2021). An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model. In: Abraham, A., et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_34

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