A Modified NSGA-DO for Solving Multiobjective Optimization Problems


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
MACHADO, Jussara Gomes; PIRES, Matheus Giovanni; BERTONI, Fabiana Cristina; PIMENTA, Adinovam Henriques de Macedo; CAMARGO, Heloisa de Arruda. A Modified NSGA-DO for Solving Multiobjective Optimization Problems. In: BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 10. , 2021, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . ISSN 2643-6264.