Speeding up COMPASS for high-dimensional discrete optimization via simulation

Hong, L. Jeff and Nelson, Barry L. and Xu, Jie (2010) Speeding up COMPASS for high-dimensional discrete optimization via simulation. Operations Research Letters, 38 (6). pp. 550-555. ISSN 0167-6377

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Abstract

The convergent optimization via most promising area stochastic search (COMPASS) algorithm is a locally convergent random search algorithm for solving discrete optimization via simulation problems. COMPASS has drawn a significant amount of attention since its introduction. While the asymptotic convergence of COMPASS does not depend on the problem dimension, the finite-time performance of the algorithm often deteriorates as the dimension increases. In this paper, we investigate the reasons for this deterioration and propose a simple change to the solution-sampling scheme that significantly speeds up COMPASS for high-dimensional problems without affecting its convergence guarantee.

Item Type:
Journal Article
Journal or Publication Title:
Operations Research Letters
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2209
Subjects:
ID Code:
65037
Deposited By:
Deposited On:
10 Jun 2013 11:08
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Mar 2020 03:28