Abstract
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.
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This work was partially financed by CONACYT, COTACYT, DGEST, TECNM, and ITCM.
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MartÃnez-Vega, D. et al. (2017). Evaluation of the Evolutionary Algorithms Performance in Many-Objective Optimization Problems Using Quality Indicators. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_42
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DOI: https://doi.org/10.1007/978-3-319-47054-2_42
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