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.

该研究探讨了优化算法中参数值对算法性能的影响，并使用萤火虫算法以及蒙特卡洛方法和拟蒙特卡洛方法进行参数调优，结果表明不同方法在优化表现上没有明显差异，显示了该算法的鲁棒性。

通过标准蒙特卡洛和准蒙特卡洛方法调整萤火虫算法的参数

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

In this study, a novel coordinative scheduling optimization approach is
proposed to enhance port efficiency by reducing average wait time and
turnaround time. The proposed approach consists of enhanced particle swarm
optimization (ePSO) as kernel and augmented firefly algorithm (AFA) as global
optimal search. Two paradigm methods of the proposed approach are investigated,
which are batch method and rolling horizon method. The experimental results
show that both paradigm methods of proposed approach can effectively enhance
port efficiency. The average wait time could be significantly reduced by 86.0%
- 95.5%, and the average turnaround time could eventually save 38.2% - 42.4%
with respect to historical benchmarks. Moreover, the paradigm method of rolling
horizon could reduce to 20 mins on running time over 3-month datasets, rather
than 4 hrs on batch method at corresponding maximum performance.

本研究提出一种新型的协调调度优化方法，通过减少平均等待时间和周转时间，提高了港口效率。实验结果表明，该方法的两种典范方法都能够有效地提高港口效率，其中滚动视界法可在 3 个月的数据集上减少到 20 分钟的运行时间。