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Asymptotically Optimal Simulation Budget Allocation under Fixed Confidence Level by Ordinal Optimization

Asymptotically Optimal Simulation Budget Allocation under Fixed Confidence Level by Ordinal Optimization
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摘要 Ordinal optimization concentrates on isolating a subset of good designs with high probability and reduces the required simulation time dramatically for discrete event simulation. To obtain the same probability level,we may optimally allocate our computing budget among different designs,instead of equally simulating all different designs. In this paper we present an effective approach to optimally allocate computing budget for discrete-event system simulation. While ordinal optimization can dramatically reduce the computation cost, our approach can further reduce the already-low cost. Ordinal optimization concentrates on isolating a subset of good designs with high probability and reduces the required simulation time dramatically for discrete event simulation. To obtain the same probability level,we may optimally allocate our computing budget among different designs,instead of equally simulating all different designs. In this paper we present an effective approach to optimally allocate computing budget for discrete-event system simulation. While ordinal optimization can dramatically reduce the computation cost, our approach can further reduce the already-low cost.
作者 王剑锋
出处 《中国民航学院学报》 2004年第B06期100-106,共7页 Journal of Civil Aviation University of China
关键词 随机优化 系统仿真 序优化 置信度 最优分配 ordinal optimization optimal computing budget allocation discrete-event simulation
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