  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.
