Title

A Fast Pareto Genetic Algorithm Approach For Solving Expensive Multiobjective Optimization Problems

Keywords

Evolutionary algorithms; Multiobjective optimization; Pareto optimality

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. © 2007 Springer Science+Business Media, LLC.

Publication Date

6-1-2008

Publication Title

Journal of Heuristics

Volume

14

Issue

3

Number of Pages

203-241

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s10732-007-9037-z

Socpus ID

42149088986 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/42149088986

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