Evolutionary multiobjective optimization in noisy problem environments

    Authors

    H. Eskandari;C. D. Geiger

    Comments

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    Abbreviated Journal Title

    J. Heuristics

    Keywords

    Multiobjective optimization; Evolutionary algorithms; Stochastic; objective function; Pareto optimality; GENETIC ALGORITHM; Computer Science, Artificial Intelligence; Computer Science, Theory &; Methods

    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.

    Journal Title

    Journal of Heuristics

    Volume

    15

    Issue/Number

    6

    Publication Date

    1-1-2009

    Document Type

    Article

    Language

    English

    First Page

    559

    Last Page

    595

    WOS Identifier

    WOS:000271422800002

    ISSN

    1381-1231

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