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Evolutionary multiobjective optimization in noisy problem environments

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Abstract

This paper presents a multiobjective evolutionary algorithm (MOEA) capable of handling stochastic objective functions. We extend a previously developed approach to solve multiple objective optimization problems in deterministic environments by incorporating a stochastic nondomination-based solution ranking procedure. In this study, concepts of stochastic dominance and significant dominance are introduced in order to better discriminate among competing solutions. The MOEA is applied to a number of published test problems to assess its robustness and to evaluate its performance relative to NSGA-II. Moreover, a new stopping criterion is proposed, which is based on the convergence velocity of any MOEA to the true Pareto optimal front, even if the exact location of the true front is unknown. This stopping criterion is especially useful in real-world problems, where finding an appropriate point to terminate the search is crucial.

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Authors and Affiliations

  1. School of Industrial Engineering, Tarbiat Modares University, Tehran, Iran

    Hamidreza Eskandari

  2. Department of Industrial Engineering and Management Systems, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA

    Christopher D. Geiger

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  1. Hamidreza Eskandari
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  2. Christopher D. Geiger
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Correspondence to Hamidreza Eskandari.

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Eskandari, H., Geiger, C.D. Evolutionary multiobjective optimization in noisy problem environments. J Heuristics 15, 559–595 (2009). https://doi.org/10.1007/s10732-008-9077-z

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  • DOI: https://doi.org/10.1007/s10732-008-9077-z

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