Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1142885 | Operations Research Letters | 2010 | 6 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Discrete Mathematics and Combinatorics
Authors
L. Jeff Hong, Barry L. Nelson, Jie Xu,