期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 62, 期 2, 页码 740-752出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2016.2570119
关键词
Feasibility detection; partition-based random search (PRS); simulation budget allocation; simulation optimization; stochastic constraints
资金
- City University of Hong Kong [7200419, 7004464]
We consider the global optimization problem over finite solution space with a deterministic objective function and stochastic constraints, where noise-corrupted observations of the constraint measures are evaluated via simulation. This problem is challenging in that the solution space often lacks rich structure that can be utilized in identifying the optimal solution, and the feasibility of a solution cannot be known for certain, due to the noisy measurements of the constraints. To tackle these two issues, we adopt a partitioning scheme to explore the solution space and develop a feasibility detection procedure to detect the feasibility of the sampled solutions. A new random search method, called partition-based random search with multi-constraint feasibility detection (PRS-MFD), is proposed. It is shown that PRS-MFD converges to the set of global optima with probability one. The significantly higher efficiency of it is demonstrated by numerical experiments.
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