Title

Evolutionary multiobjective optimization in noisy problem environments

Authors

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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