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An Efficient Sampling Approach to Multiobjective Optimization

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Abstract

This paper presents a new approach to multiobjective optimization based on the principles of probabilistic uncertainty analysis. At the core of this approach is an efficient nonlinear multiobjective optimization algorithm, Minimizing Number of Single Objective Optimization Problems (MINSOOP), to generate a true representation of the whole Pareto surface. Results show that the computational savings of this new algorithm versus the traditional constraint method increase dramatically when the number of objectives increases. A real world case study of multiobjective optimal design of a best available control technology for Nitrogen Oxides (NOx) and Sulfur Oxides (SOx) reduction illustrates the usefulness of this approach.

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Authors and Affiliations

  1. Research and Advance Engineering Laboratories, Ford Motor Company, Dearborn, MI 48124, USA

    Yan Fu

  2. CUSTOM, Center for Uncertain Systems: Tools for Optimization & Management, Departments of Bio, Chemical, and Industrial Engineering and Institute of Environmental Science & Policy, University of Illinois at Chicago, Chicago, IL 60607, USA

    Urmila M. Diwekar

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  1. Yan Fu
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  2. Urmila M. Diwekar
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Fu, Y., Diwekar, U.M. An Efficient Sampling Approach to Multiobjective Optimization. Ann Oper Res 132, 109–134 (2004). https://doi.org/10.1023/B:ANOR.0000045279.46948.dd

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