TY - JOUR
T1 - Pareto Set Selection for Multiobjective Stochastic Simulation Model
AU - Choi, Seon Han
AU - Kim, Tag Gon
N1 - Funding Information:
Manuscript received September 22, 2017; revised January 11, 2018; accepted June 8, 2018. Date of publication June 29, 2018; date of current version October 15, 2020. This work was supported in part by the Institute for Information and Communications Technology Promotion through the Korea Government (MSIP) (Development Platform for User-level Customizable, General Purpose Discrete Event Simulation Software) under Grant 2017-0-00461, and in part by the Brain Korea 21 PLUS program. This paper was recommended by Associate Editor J.-H. Chou. (Corresponding author: Seon Han Choi.) The authors are with the School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea (e-mail: gigohan01@kaist.ac.kr). Digital Object Identifier 10.1109/TSMC.2018.2846680
Publisher Copyright:
© 2018 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - Multiobjective optimization
KW - Pareto optimality
KW - ranking and selection (RandS)
KW - statistical hypothesis test
KW - stochastic simulation
UR - http://www.scopus.com/inward/record.url?scp=85049341156&partnerID=8YFLogxK
U2 - 10.1109/TSMC.2018.2846680
DO - 10.1109/TSMC.2018.2846680
M3 - Article
AN - SCOPUS:85049341156
SN - 2168-2216
VL - 50
SP - 4256
EP - 4269
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 11
M1 - 8400565
ER -