Title | ||
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Model-Based Proposal Learning for Monte Carlo Optimization of Redundancy Allocation Problem. |
Abstract | ||
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Most existing approaches to heterogeneous redundancy allocation problem (RAP) are prone to getting trapped in local optimal modes during optimization, mainly due to the rugged combinatoric landscapes. Recently, optimization-by-sampling paradigm based on the stochastic approximation Monte Carlo (SAMC) sampling has shown superior performance in solving the heterogeneous RAP for multi-state systems (MSSs). However, one drawback of this method is that the global move of a Markov chain relying only on a uniform distribution is typically hard to hit the low-energy regions due to the uninformative proposal, leading to insufficient global exploration in sampling. To address the problem of where to sample for efficient optimization of heterogeneous RAP, we introduce a rejection-free Monte Carlo method to sample from the target distribution over the combinatorial space. Specifically, a model-based proposal learning algorithm is derived to guide the global exploration towards promising regions of the descrete state space. Experimental evaluations on a set of benchmark instances show the superiority of the proposed approach compared with the several state-of-the-arts in terms of the solution quality and computational efficiency. |
Year | DOI | Venue |
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2018 | 10.1109/ACCESS.2018.2867744 | IEEE ACCESS |
Keywords | Field | DocType |
Redundancy allocation problem,multi-state system,optimization-by-sampling,Markov chain Monte Carlo,model-based proposal learning | Monte Carlo method,Mathematical optimization,Markov process,Computer science,Markov chain,Uniform distribution (continuous),Redundancy (engineering),Sampling (statistics),State space,Stochastic approximation,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Xiuzhuang Zhou | 1 | 380 | 20.26 |
Min Xu | 2 | 18 | 4.41 |