Please use this identifier to cite or link to this item: https://doi.org/10.1145/1667072.1667076
Title: Simulation optimization using the cross-entropy method with optimal computing budget allocation
Authors: He, D.
Lee, L.H. 
Chen, C.-H.
Fu, M.C.
Wasserkrug, S.
Keywords: Computing budget allocation
Cross-entropy method
Estimation of distribution algorithms
Simulation optimization
Issue Date: 1-Jan-2010
Source: He, D., Lee, L.H., Chen, C.-H., Fu, M.C., Wasserkrug, S. (2010-01-01). Simulation optimization using the cross-entropy method with optimal computing budget allocation. ACM Transactions on Modeling and Computer Simulation 20 (1) : -. ScholarBank@NUS Repository. https://doi.org/10.1145/1667072.1667076
Abstract: We propose to improve the efficiency of simulation optimization by integrating the notion of optimal computing budget allocation into the Cross-Entropy (CE) method, which is a global optimization search approach that iteratively updates a parameterized distribution from which candidate solutions are generated. This article focuses on continuous optimization problems. In the stochastic simulation setting where replications are expensive but noise in the objective function estimate could mislead the search process, the allocation of simulation replications can make a significant difference in the performance of such global optimization search algorithms. A new allocation scheme is developed based on the notion of optimal computing budget allocation. The proposed approach improves the updating of the sampling distribution by carrying out this computing budget allocation in an efficient manner, by minimizing the expected mean-squared error of the CE weight function. Numerical experiments indicate that the computational efficiency of the CE method can be substantially improved if the ideas of computing budget allocation are applied. © 2010 ACM.
Source Title: ACM Transactions on Modeling and Computer Simulation
URI: http://scholarbank.nus.edu.sg/handle/10635/63313
ISSN: 10493301
DOI: 10.1145/1667072.1667076
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