An adaptive hyperbox algorithm for high-dimensional discrete optimization via simulation problems

Jie Xu, Barry L. Nelson, L. Jeff Hong

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)133-146
Number of pages14
JournalINFORMS Journal on Computing
Issue number1
StatePublished - Dec 2013


  • Optimization via simulation
  • Random search
  • Ranking and selection

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research


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