论文标题
在基于模拟的优化中，用于启发式搜索的反应性样本量
Reactive Sample Size for Heuristic Search in Simulation-based Optimization
论文作者
论文摘要
在基于仿真的优化中，可以通过启发式优化技术确定目标函数的输入参数的最佳设置。但是，当模拟器模拟现实世界问题的随机性时，它们的输出是一个随机变量，并且对目标函数的多次评估对于正确比较了不同参数设置的预期性能。本文基于参数测试和无冷漠区域选择提供了一种新型的反应样本量算法，可用于提高启发式优化方法的效率和鲁棒性。根据观察到的统计证据，该算法以在线方式反应地决定了在优化过程中每次比较的样本量。测试采用基准功能，该功能以人工水平的噪声和用于酒店收入管理的基于模拟的优化工具扩展。实验结果表明，反应方法可以提高基于模拟的优化技术的效率和鲁棒性。
In simulation-based optimization, the optimal setting of the input parameters of the objective function can be determined by heuristic optimization techniques. However, when simulators model the stochasticity of real-world problems, their output is a random variable and multiple evaluations of the objective function are necessary to properly compare the expected performance of different parameter settings. This paper presents a novel reactive sample size algorithm based on parametric tests and indifference-zone selection, which can be used for improving the efficiency and robustness of heuristic optimization methods. The algorithm reactively decides, in an online manner, the sample size to be used for each comparison during the optimization according to observed statistical evidence. Tests employ benchmark functions extended with artificial levels of noise and a simulation-based optimization tool for hotel revenue management. Experimental results show that the reactive method can improve the efficiency and robustness of simulation-based optimization techniques.
