Parameter tuning of the Firefly Algorithm by standard Monte Carlo and Quasi-Monte Carlo methods
Conference paper
Joy, G., Huyck, C. and Yang, X. 2024. Parameter tuning of the Firefly Algorithm by standard Monte Carlo and Quasi-Monte Carlo methods. Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V., Dongarra, J. and Sloot, P. (ed.) 24th International Conference on Computational Science. Malaga, Spain 02 - 04 Jul 2024 Cham Springer. pp. 242–253 https://doi.org/10.1007/978-3-031-63775-9_17
Type | Conference paper |
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Title | Parameter tuning of the Firefly Algorithm by standard Monte Carlo and Quasi-Monte Carlo methods |
Authors | Joy, G., Huyck, C. and Yang, X. |
Abstract | Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can significantly influence the behavior of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ensure that the algorithm used for optimization performs well and is sufficiently robust for solving different types of optimization problems. In this study, the Firefly Algorithm (FA) is used to evaluate the influence of its parameter values on its efficiency. Parameter values are randomly initialized using both the standard Monte Carlo method and the Quasi Monte-Carlo method. The values are then used for tuning the FA. Two benchmark functions and a spring design problem are used to test the robustness of the tuned FA. From the preliminary findings, it can be deduced that both the Monte Carlo method and Quasi-Monte Carlo method produce similar results in terms of optimal fitness values. Numerical experiments using the two different methods on both benchmark functions and the spring design problem showed no major variations in the final fitness values, irrespective of the different sample values selected during the simulations. This insensitivity indicates the robustness of the FA. |
Keywords | Algorithm; Firefly algorithm; Parameter tuning; Monte Carlo; Quasi-Monte Carlo |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Middlesex University Theme | Creativity, Culture & Enterprise |
Research Group | Artificial Intelligence group |
Conference | 24th International Conference on Computational Science |
Page range | 242–253 |
Proceedings Title | Computational Science – ICCS 2024: 24th International Conference, Malaga, Spain, July 2–4, 2024, Proceedings, Part V |
Series | Lecture Notes in Computer Science |
Editors | Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V., Dongarra, J. and Sloot, P. |
ISSN | 0302-9743 |
Electronic | 1611-3349 |
ISBN | |
Paperback | 9783031637742 |
Electronic | 9783031637759 |
Publisher | Springer |
Place of publication | Cham |
Publication dates | |
Online | 28 Jun 2024 |
Publication process dates | |
Accepted | May 2024 |
Deposited | 02 Jul 2024 |
Output status | Published |
Accepted author manuscript | File Access Level Open |
Copyright Statement | This version of the paper has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-ma...), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-63775-9_17 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-63775-9_17 |
Web of Science identifier | WOS:001279327300017 |
Web address (URL) of conference proceedings | https://doi.org/10.1007/978-3-031-63775-9 |
Language | English |
https://repository.mdx.ac.uk/item/15x52y
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