The need to address more complex real-world problems gives rise to new research issues in many-objective optimization field. Recently, researchers have focused in developing algorithms able to solve optimization problems with more than three objectives known as many-objective optimization problems. Some methodologies have been developed into the context of this kind of problems, such as A2-NSGA-III that is an adaptive extension of the well-known NSGA-II (Non-dominated Sorting Genetic Algorithm II). A2-NSGA-III was developed for promoting a better spreading of the solutions in the Pareto front using an improved approach based on reference points. In this paper, a comparative study between NSGA-II and A2-NSGA-III is presented. We examine the performance of both algorithms by applying them to the project portfolio problem with 9 and 16 objectives. Our purpose is to validate the effectiveness of A2-NSGA-III to deal with many-objective problems and increase the variety of problems that this method can solve. Several quality indicators were used to measure the performance of the two algorithms.
CITATION STYLE
Martínez-Vega, D., Sanchez, P., Castilla, G., Fernandez, E., Cruz-Reyes, L., Gomez, C., & Martinez, E. (2017). Evaluation of the evolutionary algorithms performance in many-objective optimization problems using quality indicators. In Studies in Computational Intelligence (Vol. 667, pp. 641–653). Springer Verlag. https://doi.org/10.1007/978-3-319-47054-2_42
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