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

    Authors

    H. Eskandari;C. D. Geiger

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    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

    Share

    COinS