Name
Title | ||
|---|---|---|
Efficient Stochastic Approximation Monte Carlo Sampling for Heterogeneous Redundancy Allocation Problem. |
Abstract | ||
|---|---|---|
Existing optimization methods to heterogeneous redundancy allocation problem often suffer from the local-trap problem in optimization, due to the rugged energy landscapes. In this paper, a new optimization paradigm based on the Markov chain Monte Carlo sampling is proposed for solving the heterogeneous redundancy allocation for multi-state systems. We address this in an optimization-by-sampling framework, and propose to sample the intricate distribution over the combinatorial space by a doubly adaptive sampling approach, where the target adaptation favors free random walk on the rugged energy landscape to substantially alleviate the local-trap problem by updating the target distribution on-the-fly, while the proposal adaptation helps improve the sampling efficiency by learning the proposal distribution based on chain history in optimization. Experimental results performed on a range of benchmark instances demonstrated the superiority of the proposed optimization approach compared with the state-of-the-art alternatives in terms of the solution quality or computational efficiency. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ACCESS.2016.2611520 | IEEE ACCESS |
Keywords | Field | DocType |
Optimization-by-sampling,multi-state system,redundancy allocation problem,Markov chain Monte Carlo | Rejection sampling,Monte Carlo method,Stochastic optimization,Mathematical optimization,Global optimization,Markov chain Monte Carlo,Computer science,Hybrid Monte Carlo,Cross-entropy method,Redundancy (engineering) | Journal |
Volume | ISSN | Citations |
4 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Xu | 1 | 18 | 4.41 |
| Xiuzhuang Zhou | 2 | 380 | 20.26 |
| Qirun Huo | 3 | 2 | 1.38 |
| Haomin Liu | 4 | 0 | 0.34 |