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Simulation optimization using the cross-entropy method with optimal computing budget allocation

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Segev WasserkrugAuthors Info & Claims

ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 20, Issue 1

Article No.: 4, Pages 1 - 22

https://doi.org/10.1145/1667072.1667076

Published: 08 February 2010 Publication History

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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.

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Cited By

View all

Pedrielli GLee LChen C(2024)Stochastic Simulation Optimization with Optimal Computing Budget AllocationEncyclopedia of Optimization10.1007/978-3-030-54621-2_836-1(1-19)Online publication date: 27-Mar-2024
https://doi.org/10.1007/978-3-030-54621-2_836-1
Yu CLahrichi NMatta A(2023)Optimal budget allocation policy for tabu search in stochastic simulation optimizationComputers and Operations Research10.1016/j.cor.2022.106046150:COnline publication date: 20-Jan-2023
https://dl.acm.org/doi/10.1016/j.cor.2022.106046
Seiringer WAltendorfer KCastaneda JGayan LJuan A(2022)Potential of Simulation Effort Reduction by Intelligent Simulation Budget Management for Multi-Item and Multi-Stage Production SystemsProceedings of the Winter Simulation Conference10.5555/3586210.3586365(1864-1875)Online publication date: 11-Dec-2022
https://dl.acm.org/doi/10.5555/3586210.3586365
Show More Cited By

Index Terms

Simulation optimization using the cross-entropy method with optimal computing budget allocation
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation evaluation

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Reviews

Reviewer: Tommaso Mazza

?lafsson and Kim's work is a good introduction to this paper: Simulation optimization is the process of finding the best values of some decision variables [that is, continuous or discrete] for a system where the performance is evaluated based on the output of a simulation model of this system. ... Techniques for simulation optimization vary greatly, depending on the exact problem setting. [1] In this area, and in a continuous and noisy search-space setting, He et al. face an open issue: the development of an effective approach that can both optimize the simulation budget allocation and improve the efficiency of the cross-entropy (CE) search method. The first difficulty is the stochastic nature of evaluating such an objective function. Indeed, previous approaches are heavily hampered by noise, due to the kind of evaluation methods employed (such as stochastic simulation). One plausible workaround is to focus on how to best allocate simulation replications among candidate solutions, namely: "how much of [the] simulation budget should be allocated to additional replications at already visited points and how much to allocate for replications at newly generated points." Optimizing this tradeoff is beneficial to computational efficiency. With this in mind, the authors apply some efficient ranking and selection procedures, in order to enhance the computational efficiency of the CE algorithm. Instead of equally simulating all solutions, some distribution-based algorithms-ranging from deterministic global optimization to simulation optimization-try to allocate a larger portion of the computing budget to those candidate solutions that play a more important role. Toward this goal, He at al. investigate a different computing budget allocation approach. Using CE as the sampling method, they consider an objective designed for the performance of the CE method, intending to find an efficient computing budget allocation so that this objective can be optimized. Specifically, they derive an asymptotically optimal allocation-CE with optimal computing budget allocation (CEOCBA)-to be used just prior to the parameter updating step, at each iteration of the CE method, which minimizes the expected mean-squared error of the CE weight function (suggested in this paper). The authors apply numerical tests to four algorithms: standard CE with equal allocation, a standard version of the CEOCBA procedure, extended CE with equal allocation, and an extended version of the CEOCBA procedure. The results show that the integrated procedure can lead to significant computational efficiency gains for both CE methods, when compared to cases that do not have smarter computing budget allocation. In conclusion, even though the paper's approach was developed for the CE method, CEOCBA has the potential to be extended to other population-based evolutionary algorithms and distribution-based algorithms. The authors' proposal is well presented and clearly explained. Online Computing Reviews Service

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cover image ACM Transactions on Modeling and Computer Simulation

ACM Transactions on Modeling and Computer Simulation Volume 20, Issue 1

January 2010

145 pages

ISSN:1049-3301

EISSN:1558-1195

DOI:10.1145/1667072

Issue’s Table of Contents

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Publication History

Published: 08 February 2010

Accepted: 01 March 2009

Revised: 01 January 2009

Received: 01 November 2007

Published in TOMACS Volume 20, Issue 1

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Pedrielli GLee LChen C(2024)Stochastic Simulation Optimization with Optimal Computing Budget AllocationEncyclopedia of Optimization10.1007/978-3-030-54621-2_836-1(1-19)Online publication date: 27-Mar-2024
https://doi.org/10.1007/978-3-030-54621-2_836-1
Yu CLahrichi NMatta A(2023)Optimal budget allocation policy for tabu search in stochastic simulation optimizationComputers and Operations Research10.1016/j.cor.2022.106046150:COnline publication date: 20-Jan-2023
https://dl.acm.org/doi/10.1016/j.cor.2022.106046
Seiringer WAltendorfer KCastaneda JGayan LJuan A(2022)Potential of Simulation Effort Reduction by Intelligent Simulation Budget Management for Multi-Item and Multi-Stage Production SystemsProceedings of the Winter Simulation Conference10.5555/3586210.3586365(1864-1875)Online publication date: 11-Dec-2022
https://dl.acm.org/doi/10.5555/3586210.3586365
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Seiringer WAltendorfer KCastaneda JGayan LJuan A(2022)Potential of Simulation Effort Reduction by Intelligent Simulation Budget Management for Multi-Item and Multi-Stage Production Systems2022 Winter Simulation Conference (WSC)10.1109/WSC57314.2022.10015506(1864-1875)Online publication date: 11-Dec-2022
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