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首页> 外文期刊>Optimization and Engineering >GOPS: efficient RBF surrogate global optimization algorithm with high dimensions and many parallel processors including application to multimodal water quality PDE model calibration
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GOPS: efficient RBF surrogate global optimization algorithm with high dimensions and many parallel processors including application to multimodal water quality PDE model calibration

机译：GOP：高尺寸的高效RBF替代全局优化算法和许多并行处理器，包括应用于多模式水质PDE模型校准的应用

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This paper describes a new parallel global surrogate-based algorithm Global Optimization in Parallel with Surrogate (GOPS) for the minimization of continuous black-box objective functions that might have multiple local minima, are expensive to compute, and have no derivative information available. The task of pickingPnew evaluation points forPprocessors in each iteration is addressed by sampling around multiple center points at which the objective function has been previously evaluated. The GOPS algorithm improves on earlier algorithms by (a) new center points are selected based on bivariate non-dominated sorting of previously evaluated points with additional constraints to ensure the objective value is below a target percentile and (b) as iterations increase, the number of centers decreases, and the number of evaluation points per center increases. These strategies and the hyperparameters controlling them significantly improve GOPS's parallel performance on high dimensional problems in comparison to other global optimization algorithms, especially with a larger number of processors. GOPS is tested with up to 128 processors in parallel on 14 synthetic black-box optimization benchmarking test problems (in 10, 21, and 40 dimensions) and one 21-dimensional parameter estimation problem for an expensive real-world nonlinear lake water quality model with partial differential equations that takes 22 min for each objective function evaluation. GOPS numerically significantly outperforms (especially on high dimensional problems and with larger numbers of processors) the earlier algorithms SOP and PSD-MADS-VNS (and these two algorithms have outperformed other algorithms in prior publications).
机译：本文介绍了一个新的并联全局代理的算法全局优化与代理（GOP）并行，用于最小化可能具有多个局部最小值的连续黑盒目标函数，计算成本昂贵，并且没有可用的衍生信息。通过在先前评估目标函数的多个中心点围绕多个中心点进行采样来解决每个迭代中的PickingPnew评估点FORPProcessoR的任务。 GOPS算法通过（a）基于先前评估的点的双变量非主导排序选择新的中心点，以确保客观值低于目标百分位数和（b），因为迭代的增加，因此中心降低，每个中心的评估点数增加。与其他全局优化算法相比，这些策略和控制它们的策略和超参数显着提高了GOPS对高维问题的平行性能，尤其是具有更大数量的处理器。在14个合成黑匣子优化基准测试问题（10,21和40维）和一个21维参数估计问题上并行测试GOP，并与为每个客观函数评估需要22分钟的部分微分方程。 GOP在数值上显着优于胜过（特别是在高维问题和更大数量的处理器上）早期的算法SOP和PSD-MYS-VNS（以及这两个算法在先前出版物中已经表现出其他算法）。

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