A Modified NSGA-DO for Solving Multiobjective Optimization Problems

• Jussara Gomes Machado UEFS https://orcid.org/0000-0001-6899-0539
• Matheus Giovanni Pires UEFS https://orcid.org/0000-0003-1998-0304
• Fabiana Cristina Bertoni UEFS https://orcid.org/0000-0001-7759-460X
• Adinovam Henriques de Macedo Pimenta UNASP https://orcid.org/0000-0001-9931-5763
• Heloisa de Arruda Camargo UFSCar https://orcid.org/0000-0002-5489-7306

Resumo

This paper presents a novel Multiobjective Genetic Algorithm, named Modified Non-Dominated Sorting Genetic Algorithm Distance Oriented (MNSGA-DO), which aims to adjust the NSGA-DO selection operator to improve its diversity when applied to continuous multiobjective optimization problems. In order to validate this new Genetic Algorithm, we carried out a performance comparison among it and the genetic algorithms NSGA-II and NSGA-DO, regarding continuous multiobjective optimization problems. To this aim, a set of standard benchmark problems, the so-called ZDT functions, was applied considering the quality indicators Generational Distance, Inverted Generational Distance and Hypervolume as well as a time evaluation. The results demonstrate that MNSGA-DO overcomes NSGA-II and NSGA-DO in almost all benchmarks, obtaining more accurate solutions and diversity.

Palavras-chave: Multiobjective genetic algorithm, Multiobjective optimization, NSGA-II, NSGA-DO