DISCRETE OPTIMIZATION VIA APPROXIMATE ANNEALING ADAPTIVE SEARCH WITH STOCHASTIC AVERAGING

被引:0
|
作者
Hu, Jiaqiao [1 ]
Wang, Chen [1 ]
机构
[1] SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY 11794 USA
来源
PROCEEDINGS OF THE 2011 WINTER SIMULATION CONFERENCE (WSC) | 2011年
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We propose a random search algorithm for black-box optimization with discrete decision variables. The algorithm is based on the recently introduced Model-based Annealing Random Search (MARS) for global optimization, which samples candidate solutions from a sequence of iteratively focusing distribution functions over the solution space. In contrast with MARS, which requires a sample size (number of candidate solutions) that grows at least polynomially with the number of iterations for convergence, our approach employs a stochastic averaging idea and uses only a small constant number of candidate solutions per iteration. We establish global convergence of the proposed algorithm and provide numerical examples to illustrate its performance.
引用
收藏
页码:4201 / 4211
页数:11
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