Improving NSGA-II Algorithm Based on Minimum Spanning Tree

Authors:
Miqing Li;Jinhua Zheng;Jun Wu
Affiliations:
Institute of Information Engineering, Xiangtan University, Hunan, China 411105;Institute of Information Engineering, Xiangtan University, Hunan, China 411105;Institute of Information Engineering, Xiangtan University, Hunan, China 411105
Venue:
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Year:
2008

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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.