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An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems

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An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems. / Xu, J.; Nelson, B. L.; Hong, L. J.
In: INFORMS Journal on Computing, Vol. 25, No. 1, 2013, p. 133-146.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Xu, J, Nelson, BL & Hong, LJ 2013, 'An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems', INFORMS Journal on Computing, vol. 25, no. 1, pp. 133-146. https://doi.org/10.1287/ijoc.1110.0481

APA

Xu, J., Nelson, B. L., & Hong, L. J. (2013). An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems. INFORMS Journal on Computing, 25(1), 133-146. https://doi.org/10.1287/ijoc.1110.0481

Vancouver

Xu J, Nelson BL, Hong LJ. An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems. INFORMS Journal on Computing. 2013;25(1):133-146. doi: 10.1287/ijoc.1110.0481

Author

Xu, J. ; Nelson, B. L. ; Hong, L. J. / An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems. In: INFORMS Journal on Computing. 2013 ; Vol. 25, No. 1. pp. 133-146.

Bibtex

@article{7d13229bcc894f17b60fa1a943d658f2,
title = "An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems",
abstract = "We propose an adaptive hyperbox algorithm (AHA), which is an instance of a locally convergent, random search algorithm for solving discrete optimization via simulation problems. Compared to the COMPASS algorithm, AHA is more efficient in high-dimensional problems. By analyzing models of the behavior of COMPASS and AHA, we show why COMPASS slows down significantly as dimension increases, whereas AHA is less affected. Both AHA and COMPASS can be used as the local search algorithm within the Industrial Strength COMPASS framework, which consists of a global search phase, a local search phase, and a final cleanup phase. We compare the performance of AHA to COMPASS within the framework of Industrial Strength COMPASS and as stand-alone algorithms. Numerical experiments demonstrate that AHA scales up well in high-dimensional problems and has similar performance to COMPASS in low-dimensional problems.",
keywords = "optimization via simulation random search ranking and selection, random search , ranking and selection",
author = "J. Xu and Nelson, {B. L.} and Hong, {L. J.}",
year = "2013",
doi = "10.1287/ijoc.1110.0481",
language = "English",
volume = "25",
pages = "133--146",
journal = "INFORMS Journal on Computing",
issn = "1091-9856",
publisher = "INFORMS Inst.for Operations Res.and the Management Sciences",
number = "1",

}

RIS

TY - JOUR

T1 - An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems

AU - Xu, J.

AU - Nelson, B. L.

AU - Hong, L. J.

PY - 2013

Y1 - 2013

N2 - We propose an adaptive hyperbox algorithm (AHA), which is an instance of a locally convergent, random search algorithm for solving discrete optimization via simulation problems. Compared to the COMPASS algorithm, AHA is more efficient in high-dimensional problems. By analyzing models of the behavior of COMPASS and AHA, we show why COMPASS slows down significantly as dimension increases, whereas AHA is less affected. Both AHA and COMPASS can be used as the local search algorithm within the Industrial Strength COMPASS framework, which consists of a global search phase, a local search phase, and a final cleanup phase. We compare the performance of AHA to COMPASS within the framework of Industrial Strength COMPASS and as stand-alone algorithms. Numerical experiments demonstrate that AHA scales up well in high-dimensional problems and has similar performance to COMPASS in low-dimensional problems.

AB - We propose an adaptive hyperbox algorithm (AHA), which is an instance of a locally convergent, random search algorithm for solving discrete optimization via simulation problems. Compared to the COMPASS algorithm, AHA is more efficient in high-dimensional problems. By analyzing models of the behavior of COMPASS and AHA, we show why COMPASS slows down significantly as dimension increases, whereas AHA is less affected. Both AHA and COMPASS can be used as the local search algorithm within the Industrial Strength COMPASS framework, which consists of a global search phase, a local search phase, and a final cleanup phase. We compare the performance of AHA to COMPASS within the framework of Industrial Strength COMPASS and as stand-alone algorithms. Numerical experiments demonstrate that AHA scales up well in high-dimensional problems and has similar performance to COMPASS in low-dimensional problems.

KW - optimization via simulation random search ranking and selection

KW - random search

KW - ranking and selection

U2 - 10.1287/ijoc.1110.0481

DO - 10.1287/ijoc.1110.0481

M3 - Journal article

VL - 25

SP - 133

EP - 146

JO - INFORMS Journal on Computing

JF - INFORMS Journal on Computing

SN - 1091-9856

IS - 1

ER -