Discrete Optimization via Simulation Using COMPASS

Li, Hong; Nelson, Barry L.

doi:10.1287/opre.1050.0237

Cited by 255 publications

(163 citation statements)

References 29 publications

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“…These results are similar to the results that we present in Table 2. Results for the asymptotic test derived in [3] are presented in [16], using m = 20 (instead of m = 3) replicates, a …rst-order polynomial (instead of the second-order polynomial in equation 8), and only 100 (instead of 500) macroreplicates. Now the probability of rejecting the points B through E (not A) is much higher: increasing the number of replicates increases the power of the test!…”

Section: Kkt Tests: Resultsmentioning

confidence: 99%

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Optimization of Simulated Inventory Systems: OptQuest and Alternatives

Kleijnen

Wan

2006

SSRN Journal

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Section: Kkt Tests: Resultsmentioning

confidence: 99%

“…OptQuest combines the metaheuristics of Tabu Search, Neural Networks, and Scatter Search into a single search heuristic; also see the recent publications [1], [4], [8], and [14]. OptQuest is provided (free of charge) with the student version of the Arena software; see [10].…”

Section: Optquestmentioning

confidence: 99%

Optimization of Simulated Inventory Systems: OptQuest and Alternatives

Kleijnen

Wan

2006

SSRN Journal

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“…Convergent optimization via most-promising-area stochastic search (COMPASS) [17] sounds similar, but it is a technique for discrete optimization via simulation, where "there is no explicit form of the objective function, and function evaluations are stochastic and computationally expensive." Predictably, it always samples the most promising area next, rather than select it probabilistically using scores, and stochastic search refers to uniform sampling within that area.…”

Section: Related Workmentioning

confidence: 99%

The Revised Stochastic Simplex Bisection Algorithm and Particle Swarm Optimization

Samuelsson¹

2017

Annals of Computer Science and Information Systems

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Abstract-The stochastic simplex bisection (SSB) algorithm is evaluated against particle swarm optimization (PSO) on a prominent test set. The original SSB algorithm performs on par with the PSO algorithm and a revised version of the SSB algorithm outperforms both of them. Detailed analysis of the performance on select objective functions brings to light key properties of the three algorithms. The core SSB algorithm is here viewed as a sampling tool for an outer loop that employs statistical pattern recognition. This opens the door for a host of other schemes.

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“…that satisfy the large deviations principle (cf. e.g., [16], [32]). Assumption L2 can be viewed as a simple extension of L1.…”

Section: Where φ(· ·) Satisfies the Conditions In L1mentioning

confidence: 99%

A Model Reference Adaptive Search Method for Stochastic Global Optimization

Hu¹,

Fu²,

Marcus³

2008

Communications in Information and Systems

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We propose a randomized search method called Stochastic Model Reference Adaptive Search (SMRAS) for solving stochastic optimization problems in situations where the objective functions cannot be evaluated exactly, but can be estimated with some noise (or uncertainty), e.g., via simulation. The method generalizes the recently proposed Model Reference Adaptive Search (MRAS) method for deterministic optimization, and is based on sampling from an underlying probability distribution "model" on the solution space, which is updated iteratively after evaluating the performance of the samples at each iteration. We prove global convergence of SMRAS in a general stochastic setting, and carry out numerical studies to illustrate its performance. An emphasis of this paper is on the application of SMRAS for solving static stochastic optimization problems with either continuous or discrete domains; its various applications for solving dynamic decision making problems can be found in [6].

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Discrete Optimization via Simulation Using COMPASS

Cited by 255 publications

References 29 publications

Optimization of Simulated Inventory Systems: OptQuest and Alternatives

Optimization of Simulated Inventory Systems: OptQuest and Alternatives

The Revised Stochastic Simplex Bisection Algorithm and Particle Swarm Optimization

A Model Reference Adaptive Search Method for Stochastic Global Optimization

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