An Efficient Sampling Approach to Multiobjective Optimization

Yan Fu () and Urmila Diwekar ()

Annals of Operations Research, 2004, vol. 132, issue 1, 109-134

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. Copyright Kluwer Academic Publishers 2004

Keywords: nonlinear multiobjective optimization; MINSOOP algorithm; Hammersley sequence sampling; LTO process; NOx reduction (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1023/B:ANOR.0000045279.46948.dd

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