A NEW FRAMEWORK FOR PARALLEL RANKING & SELECTION USING AN ADAPTIVE STANDARD
A NEW FRAMEWORK FOR PARALLEL RANKING & SELECTION USING AN ADAPTIVE STANDARD
复制标题
平行排名的新框架
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
S. R. Hunter
中科院分区：
文献类型：
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作者：
Linda Pei;B. Nelson;S. R. Hunter
当我们有足够的计算资源将模拟优化问题视为排序与选择（R&S）问题时，它就可以被“解决”。R&S是穷举搜索——对所有可行解进行模拟——并具有有意义的统计误差控制。高性能并行计算有望将R&S的极限扩展到更大的问题，但以一种在实现大幅加速的同时保持统计有效性的方式并行化R&S程序是困难的。在本文中，我们引入了一种全新的R&S框架，称为并行自适应幸存者选择（PASS），它是专门为利用并行计算环境来解决具有大量可行解的模拟优化问题而设计的。
When we have sufficient computational resources to treat a simulation optimization problem as a ranking & selection (R&S) problem, then it can be "solved." R&S is exhaustive search—all feasible solutions are simulated—with meaningful statistical error control. High-performance parallel computing promises to extend the R&S limit to even larger problems, but parallelizing R&S procedures in a way that maintains statistical validity while achieving substantial speed-up is difficult. In this paper we introduce an entirely new framework for R&S called Parallel Adaptive Survivor Selection (PASS) that is specifically engineered to exploit parallel computing environments for solving simulation optimization problems with a very large number of feasible solutions.