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

TL;DR

This paper investigates the impact of parameter tuning on the Firefly Algorithm's performance using Monte Carlo and Quasi-Monte Carlo methods, demonstrating robustness across various test problems.

Contribution

It introduces a novel approach to parameter tuning of the Firefly Algorithm using Monte Carlo and Quasi-Monte Carlo methods, showing their effectiveness and resulting robustness.

Findings

Both methods yield similar optimal fitness values.

The Firefly Algorithm shows insensitivity to parameter variations.

Robustness is confirmed across benchmark and real-world problems.

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.