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Model-Based Proposal Learning for Monte Carlo Optimization of Redundancy Allocation Problem
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DOI：10.1109/ACCESS.2018.2867744.png)
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摘要
En 中文
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.
Keyword:
Redundancy allocation problem
multi-state system
optimization-by-sampling
Markov chain Monte Carlo
model-based proposal learning
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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