Abstract
Ordinal optimization is a tool to reduce the computational burden in simulation-based optimization problems. So far, the major effort in this field focuses on single-objective optimization. In this paper, we extend this to multiobjective optimization and develop vector ordinal optimization, which is different from the one introduced in Ref. 1. Alignment probability and ordered performance curve (OPC) are redefined for multiobjective optimization. Our results lead to quantifiable subset selection sizes in the multiobjective case, which supplies guidance in solving practical problems, as demonstrated by the examples in this paper.
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This paper was supported in part by Army Contract DAAD19-01-1-0610, AFOSR Contract F49620-01-1-0288, and a contract with United Technology Research Center (UTRC). The first author received additional funding from NSF of China Grants 60074012 and 60274011, Ministry of Education (China), and a Tsinghua University (Beijing, China) Fundamental Research Funding Grant, and the NCET program of China.
The authors are grateful to and benefited from two rounds of reviews from three anonymous referees.
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Zhao, Q.C., Ho, Y.C. & Jia, Q.S. Vector Ordinal Optimization. J Optim Theory Appl 125, 259–274 (2005). https://doi.org/10.1007/s10957-004-1837-9
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DOI: https://doi.org/10.1007/s10957-004-1837-9