Title

A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems

Authors

Authors

H. Eskandari;C. D. Geiger

Comments

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Abbreviated Journal Title

J. Heuristics

Keywords

multiobjective optimization; evolutionary algorithms; Pareto optimality; EVOLUTIONARY ALGORITHMS; Computer Science, Artificial Intelligence; Computer Science, Theory &; Methods

Abstract

We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FastPGA), for the simultaneous optimization of multiple objectives where each solution evaluation is computationally- and/or financially-expensive. This is often the case when there are time or resource constraints involved in finding a solution. FastPGA utilizes a new ranking strategy that utilizes more information about Pareto dominance among solutions and niching relations. New genetic operators are employed to enhance the proposed algorithm's performance in terms of convergence behavior and computational effort as rapid convergence is of utmost concern and highly desired when solving expensive multiobjective optimization problems (MOPs). Computational results for a number of test problems indicate that FastPGA is a promising approach. FastPGA yields similar performance to that of the improved nondominated sorting genetic algorithm (NSGA-II), a widely-accepted benchmark in the MOEA research community. However, FastPGA outperforms NSGA-II when only a small number of solution evaluations are permitted, as would be the case when solving expensive MOPs.

Journal Title

Journal of Heuristics

Volume

14

Issue/Number

3

Publication Date

1-1-2008

Document Type

Article

Language

English

First Page

203

Last Page

241

WOS Identifier

WOS:000254904500001

ISSN

1381-1231

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