A Modified NSGA-DO for Solving Multiobjective Optimization Problems

Author / Creator

BRACIS 2021 (2021)

Conferences

BRACIS 2021 Metaheuristics and Optimization I (2021)

Available as

Online

Summary

This paper presents a novel Multiobjective Genetic Algorithm, named Modied Non-Dominated Sorting Genetic Algorithm Distance Oriented (MNSGA-DO), which aims to adjust the NSGA-DO selection operator ...

This paper presents a novel Multiobjective Genetic Algorithm, named Modied 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.

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