A Modified NSGA-DO for Solving Multiobjective Optimization Problems - Machado, Jussara Gomes; Pires, Matheus Giovanni; Bertoni, Fabiana Cristina; Pimenta, Adinovam Henriques de Macedo; Camargo, Heloisa de Arruda (Anais da Brazilian Conference on Intelligent Systems (BRACIS); 2021: Anais da X Brazilian Conference on Intelligent Systems, 2021)
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.