Abstract
The convergence and the diversity of the decomposition-based evolutionary algorithm Global WASF-GA (GWASF-GA) relies on a set of weight vectors that determine the search directions for new non-dominated solutions in the objective space. Although using weight vectors whose search directions are widely distributed may lead to a well-diversified approximation of the Pareto front (PF), this may not be enough to obtain a good approximation for complicated PFs (discontinuous, non-convex, etc.). Thus, we propose to dynamically adjust the weight vectors once GWASF-GA has been run for a certain number of generations. This adjustment is aimed at re-calculating some of the weight vectors, so that search directions pointing to overcrowded regions of the PF are redirected toward parts with a lack of solutions that may be hard to be approximated. We test different parameters settings of the dynamic adjustment in optimization problems with three, five, and six objectives, concluding that GWASF-GA performs better when adjusting the weight vectors dynamically than without applying the adjustment.
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Notes
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These values reported the best results after performing several initial tests. The results are not included due to space limitations, but they are available upon request.
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We use the wilcox.test function from the R software available at https://stat.ethz.ch/R-manual/R-devel/library/stats/html/wilcox.test.html.
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Acknowledgements
This research is funded by the Spanish government (ECO2017-88883-R and ECO2017-90573-REDT) and by the Andalusian regional government (SEJ-532). Sandra González-Gallardo has a technical research contract within “Sistema Nacional de Garantía Juvenil y del Programa Operativo de Empleo Juvenil 2014-2020 - Fondos FEDER”, and thanks the University of Málaga PhD Programme in Economy and Business. Rubén Saborido is a post-doctoral fellow at Concordia University (Canada). Ana B. Ruiz thanks the post-doctoral fellowship “Captación de Talento para la Investigación” of the University of Málaga.
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González-Gallardo, S., Saborido, R., Ruiz, A.B., Luque, M. (2018). An Improvement Study of the Decomposition-Based Algorithm Global WASF-GA for Evolutionary Multiobjective Optimization. In: Herrera, F., et al. Advances in Artificial Intelligence. CAEPIA 2018. Lecture Notes in Computer Science(), vol 11160. Springer, Cham. https://doi.org/10.1007/978-3-030-00374-6_21
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