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

Xu, J. and Nelson, B. L. and Hong, L. J. (2013) An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems. INFORMS Journal on Computing, 25 (1). pp. 133-146. ISSN 1091-9856

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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.

Item Type:
Journal Article
Journal or Publication Title:
INFORMS Journal on Computing
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1706
Subjects:
ID Code:
65034
Deposited By:
Deposited On:
07 Jun 2013 08:53
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Jul 2020 01:15