Pareto set selection for multiobjective stochastic simulation model

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This paper addresses the problem of selecting a Pareto set from among finite alternatives, where each alternative has multiple performance measures evaluated by stochastic simulations. Under limited simulation resources, we propose an efficient algorithm for solving this problem based on a statistical hypothesis test. Using the test, the proposed algorithm evaluates the uncertainty of each design based on the observed simulation results to identify whether the selected Pareto set is accurate. Based on the evaluated uncertainty, the algorithm assigns additional resources to the designs to maximize the accuracy of the selected Pareto set. Applying the sequential procedure, the algorithm increases the precision of the observed information selectively and gradually. Several experiments, including a practical case study, demonstrated its improved efficiency compared to the existing algorithms in the literature. This improved efficiency, along with low complexity and high robustness to noise, allows the proposed algorithm to be effectively applied to practical system designs.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-11
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, v.50, no.11, pp.4256 - 4269

ISSN
2168-2216
DOI
10.1109/TSMC.2018.2846680
URI
http://hdl.handle.net/10203/277082
Appears in Collection
EE-Journal Papers(저널논문)
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