Abstract
Diversity preservation is a critical issue in evolutionary multi-objective optimization algorithms (MOEAs), it has significant influence on the quality of final solution set. In this wok, a crowding density measurement is developed for preserving diversity in MOEAs by using the Delaunay triangulation mesh built on the population in the objective space. Base on the property of the Delaunay triangulation, the new density measurement considers both the Euclidean distance and the relative position between individuals, and thus provide a more accurate estimation of the density around a specific individual within the population. Experimental results indicate that the suggested density measurement help to improve the performance of MOEAs significantly.
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Qi, Y., Yin, M., Li, X. (2016). A Delaunay Triangulation Based Density Measurement for Evolutionary Multi-objective Optimization. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_16
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DOI: https://doi.org/10.1007/978-3-319-28270-1_16
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