Title

Evolutionary Multiobjective Optimization In Noisy Problem Environments

Keywords

Evolutionary algorithms; Multiobjective optimization; Pareto optimality; Stochastic objective function

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. © 2008 Springer Science+Business Media, LLC.

Publication Date

12-1-2009

Publication Title

Journal of Heuristics

Volume

15

Issue

6

Number of Pages

559-595

Document Type

Article

Personal Identifier

scopus

DOI Link

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

Socpus ID

70449523355 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/70449523355

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