Search Techniques for Multi-Objective Optimization of Mixed-Variable Systems Having Stochastic Responses

Walston, J.G. (2007). Search Techniques for Multi-Objective Optimization of Mixed-Variable Systems Having Stochastic Responses. IIASA Interim Report. IIASA, Laxenburg, Austria: IR-07-014

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Abstract

A method is proposed for solving stochastic multi-objective optimization problems. Such problems are typically encountered when one desires to optimize systems with multiple, often competing, objectives that do not have a closed form representation and must be estimated via simulation. A two-stage method is proposed that combines generalized pattern search/ranking and selection (GPS/R&S) and and Mesh Adaptive Direct Search (MADS) developed for single-objective stochastic problems with three multi-objective methods: interactive techniques for the specification of aspiration/reservation levels, scalarization functions, and multi-objective ranking and selection. This combination is devised specifically so as to keep the desirable convergence properties of GPS/R&S and MADS while extending application to the multi-objective case.

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