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.

翻译：几乎所有的优化算法都具有依赖于算法自身的参数，而这些参数值的设定会显著影响所考虑算法的行为表现。因此，需要进行适当的参数调优，以确保用于优化的算法性能良好，并且对于求解不同类型的优化问题具有足够的鲁棒性。在本研究中，使用萤火虫算法（FA）来评估其参数值对算法效率的影响。参数值分别采用标准蒙特卡洛方法和拟蒙特卡洛方法进行随机初始化，随后将这些值用于FA的调优。通过两个基准测试函数和一个弹簧设计问题来测试调优后FA的鲁棒性。初步结果表明，蒙特卡洛方法与拟蒙特卡洛方法在获得的最优适应度值方面产生了相似的结果。在两个基准函数和弹簧设计问题上使用这两种不同方法进行的数值实验显示，无论模拟过程中选择何种不同的样本值，最终适应度值均未出现显著差异。这种不敏感性表明了FA的鲁棒性。