Abstract
This paper investigates the scalability of the Delaunay triangulation (DT) based diversity preservation technique for solving many-objective optimization problems (MaOPs). Following the NSGA-II algorithm, the proposed optimizer with DT based density measurement (NSGAII-DT) determines the density of individuals according to the DT mesh built on the population in the objective space. To reduce the computing time, the population is projected onto a plane before building the DT mesh. Experimental results show that NSGA-II-DT outperforms NSGA-II on WFG problems with 4, 5 and 6 objectives. Two projection strategies using a unit plane and a least-squares plane in the objective space are investigated and compared. Our results also show that the former is more effective than the latter.
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Qi, Y., Guo, H., Li, X. (2017). Extending the Delaunay Triangulation Based Density Measurement to Many-Objective Optimization. In: Wagner, M., Li, X., Hendtlass, T. (eds) Artificial Life and Computational Intelligence. ACALCI 2017. Lecture Notes in Computer Science(), vol 10142. Springer, Cham. https://doi.org/10.1007/978-3-319-51691-2_1
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DOI: https://doi.org/10.1007/978-3-319-51691-2_1
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