Parameter tuning of the firefly algorithm by three tuning methods: Standard Monte Carlo, quasi-Monte Carlo and latin hypercube sampling methods

There are many different nature-inspired algorithms in the literature, and almost all such algorithms have algorithm-dependent parameters that need to be tuned. The proper setting and parameter tuning should be carried out to maximize the performance of the algorithm under consideration. This work i...

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Vydané v:Journal of computational science Ročník 87; s. 102588
Hlavní autori: Joy, Geethu, Huyck, Christian, Yang, Xin-She
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.05.2025
Predmet:
Algorithm
Firefly algorithm
Latin hypercube sampling
Monte Carlo method
Optimization
Parameter tuning
Monte Carlo method
Latin hypercube sampling
Algorithm
Firefly algorithm
Parameter tuning
Optimization
ISSN:1877-7503
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Popis
Shrnutí:There are many different nature-inspired algorithms in the literature, and almost all such algorithms have algorithm-dependent parameters that need to be tuned. The proper setting and parameter tuning should be carried out to maximize the performance of the algorithm under consideration. This work is the extension of the recent work on parameter tuning by Joy et al. (2024) presented at the International Conference on Computational Science (ICCS 2024), and the Firefly Algorithm (FA) is tuned using three different methods: the Monte Carlo method, the Quasi-Monte Carlo method and the Latin Hypercube Sampling. The FA with the tuned parameters is then used to solve a set of six different optimization problems, and the possible effect of parameter setting on the quality of the optimal solutions is analyzed. Rigorous statistical hypothesis tests have been carried out, including Student’s t-tests, F-tests, non-parametric Friedman tests and ANOVA. Results show that the performance of the FA is not influenced by the tuning methods used. In addition, the tuned parameter values are largely independent of the tuning methods used. This indicates that the FA can be flexible and equally effective in solving optimization problems, and any of the three tuning methods can be used to tune its parameters effectively. [Display omitted] •Tuning parameters of the firefly algorithm using three tuning methods: Monte Carlo, Quasi-Monte Carlo and Latin hypercube sampling.•Comprehensive statistical analysis using Student t-tests, F-tests, Friedman tests and ANOVA.•Tuned parameter values and solution quality are independent of the tuning method used.
ISSN:1877-7503
DOI:10.1016/j.jocs.2025.102588