Abstract
Diversity maintenance is an importance part of multi-objective evolutionary algorithm. In this paper, a new variant for the NSGA-II algorithm is proposed. The basic idea is that using the crowding distance method designed by minimum spanning tree to maintain the distribution of solutions. From an extensive comparative study with NSGA-II on a number of two and three objective test problems, it is observed that the proposed algorithm has good performance in distribution, and is also rather competitive to NSGA-II concerning the convergence. © 2008 Springer Berlin Heidelberg.
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CITATION STYLE
Li, M., Zheng, J., & Wu, J. (2008). Improving NSGA-II algorithm based on minimum spanning tree. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5361 LNAI, pp. 170–179). https://doi.org/10.1007/978-3-540-89694-4_18
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