An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model

Mauricio Castillo; Broderick Crawford; Ricardo Soto; Wenceslao Palma; José Lemus-Romani; Diego Tapia; Felipe Cisternas-Caneo; Marcelo Becerra-Rozas; Fernando Paredes; Sanjay Misra

doi:10.1007/978-3-030-73689-7_34

An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model

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

SCHOOL OF COMPUTER ENGINEERING

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020
Editors	Ajith Abraham, Yukio Ohsawa, Niketa Gandhi, M. A. Jabbar, Abdelkrim Haqiq, Seán McLoone, Biju Issac
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	354-363
Number of pages	10
ISBN (Print)	9783030736880
DOIs	https://doi.org/10.1007/978-3-030-73689-7_34
State	Published - 2021
Event	12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020 - Virtual, Online Duration: 15 Dec 2020 → 18 Dec 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1383 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020
City	Virtual, Online
Period	15/12/20 → 18/12/20

Keywords

Galactic Swarm Optimization
Hidden Markov model
Machine learning
Metaheuristics
Parameter control

Access to Document

10.1007/978-3-030-73689-7_34

Cite this

Castillo, M., Crawford, B., Soto, R., Palma, W., Lemus-Romani, J., Tapia, D., Cisternas-Caneo, F., Becerra-Rozas, M., Paredes, F., & Misra, S. (2021). An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model. In A. Abraham, Y. Ohsawa, N. Gandhi, M. A. Jabbar, A. Haqiq, S. McLoone, & B. Issac (Eds.), Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 (pp. 354-363). (Advances in Intelligent Systems and Computing; Vol. 1383 AISC). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-73689-7_34

Castillo, Mauricio ; Crawford, Broderick ; Soto, Ricardo et al. / An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020. editor / Ajith Abraham ; Yukio Ohsawa ; Niketa Gandhi ; M. A. Jabbar ; Abdelkrim Haqiq ; Seán McLoone ; Biju Issac. Springer Science and Business Media Deutschland GmbH, 2021. pp. 354-363 (Advances in Intelligent Systems and Computing).

@inproceedings{394dbead75fb45a6847cfcd8bb49d70f,

title = "An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model",

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.",

keywords = "Galactic Swarm Optimization, Hidden Markov model, Machine learning, Metaheuristics, Parameter control",

author = "Mauricio Castillo and Broderick Crawford and Ricardo Soto and Wenceslao Palma and Jos{\'e} Lemus-Romani and Diego Tapia and Felipe Cisternas-Caneo and Marcelo Becerra-Rozas and Fernando Paredes and Sanjay Misra",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020 ; Conference date: 15-12-2020 Through 18-12-2020",

year = "2021",

doi = "10.1007/978-3-030-73689-7_34",

language = "English",

isbn = "9783030736880",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "354--363",

editor = "Ajith Abraham and Yukio Ohsawa and Niketa Gandhi and Jabbar, {M. A.} and Abdelkrim Haqiq and Se{\'a}n McLoone and Biju Issac",

booktitle = "Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020",

}

Castillo, M, Crawford, B , Soto, R , Palma, W, Lemus-Romani, J, Tapia, D, Cisternas-Caneo, F, Becerra-Rozas, M, Paredes, F & Misra, S 2021, An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model. in A Abraham, Y Ohsawa, N Gandhi, MA Jabbar, A Haqiq, S McLoone & B Issac (eds), Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020. Advances in Intelligent Systems and Computing, vol. 1383 AISC, Springer Science and Business Media Deutschland GmbH, pp. 354-363, 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020, Virtual, Online, 15/12/20. https://doi.org/10.1007/978-3-030-73689-7_34

An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model. / Castillo, Mauricio; Crawford, Broderick ; Soto, Ricardo et al.
Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020. ed. / Ajith Abraham; Yukio Ohsawa; Niketa Gandhi; M. A. Jabbar; Abdelkrim Haqiq; Seán McLoone; Biju Issac. Springer Science and Business Media Deutschland GmbH, 2021. p. 354-363 (Advances in Intelligent Systems and Computing; Vol. 1383 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model

AU - Castillo, Mauricio

AU - Crawford, Broderick

AU - Soto, Ricardo

AU - Palma, Wenceslao

AU - Lemus-Romani, José

AU - Tapia, Diego

AU - Cisternas-Caneo, Felipe

AU - Becerra-Rozas, Marcelo

AU - Paredes, Fernando

AU - Misra, Sanjay

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

T2 - 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020

Y2 - 15 December 2020 through 18 December 2020

ER -

Castillo M, Crawford B , Soto R , Palma W, Lemus-Romani J, Tapia D et al. An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model. In Abraham A, Ohsawa Y, Gandhi N, Jabbar MA, Haqiq A, McLoone S, Issac B, editors, Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020. Springer Science and Business Media Deutschland GmbH. 2021. p. 354-363. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-73689-7_34

An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this