Title
A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems
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
ISSN
1381-1231
Recommended Citation
"A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems" (2008). Faculty Bibliography 2000s. 305.
https://stars.library.ucf.edu/facultybib2000/305
Comments
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