# Parameter Tuning of the Firefly Algorithm by Standard Monte Carlo and Quasi-Monte Carlo Methods

arXiv: https://arxiv.org/abs/2407.02537 · HTML: https://mlsanity.com/paper/2407.02537/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [A Literature Survey of Benchmark Functions For Global Optimization Problems](https://mlsanity.com/paper/1308.4008/) (2013)
- [A Framework for Self-Tuning Optimization Algorithm](https://mlsanity.com/paper/1312.5667/) (2013)
- [A Discrete Firefly Algorithm to Solve a Rich Vehicle Routing Problem Modelling a Newspaper Distribution System with Recycling Policy](https://mlsanity.com/paper/1604.04146/) (2016)
- [Review of Parameter Tuning Methods for Nature-Inspired Algorithms](https://mlsanity.com/paper/2308.15965/) (2023)
- [Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team](https://mlsanity.com/paper/1903.11621/) (2019)

## Built on by (most cited first, 1 of 1)

- [Parameter Tuning of the Firefly Algorithm by Three Tuning Methods: Standard Monte Carlo, Quasi-Monte Carlo and Latin Hypercube Sampling Methods](https://mlsanity.com/paper/2504.18545/) (2025)

## Abstract (arXiv)

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.