This paper proposes an improved algorithm for the optimal subset selection of a stochastic simulation model. The algorithm uses a statistical hypothesis test based on frequentist inference to evaluate the uncertainty about the selection, and it distributes simulation resources to designs for minimizing the uncertainty in each iteration. Several experiments demonstrate the improved performance compared to the other algorithms, and the performance increases significantly as the noise of the model increases. As a result, its high robustness to noise allows the algorithm to efficiently analyze real-world problems.
|Number of pages||8|
|Journal||IEEE Transactions on Systems, Man, and Cybernetics: Systems|
|State||Published - Apr 2018|
Bibliographical notePublisher Copyright:
© 2013 IEEE.
- Optimal subset selection
- ranking and selection (R&S)
- simulation-based optimization (SBO)
- statistical hypothesis test
- stochastic model