Niche-based and angle-based selection strategies for many-objective evolutionary optimization
It is well known that balancing population diversity and convergence plays a crucial role in evolutionary many-objective optimization. However, most existing multiobjective evolutionary algorithms encounter difficulties in solving many-objective optimization problems. Thus, this paper suggests niche-based and angle-based selection strategies for many-objective evolutionary optimization. In the proposed algorithm, two strategies are included: niche-based density estimation strategy and angle-based selection strategy. Both strategies are employed in the environmental selection to eliminate the worst individual from the population in an iterative way. To be specific, the former estimates the diversity of each individual and finds the most crowded area in the population. The latter removes individuals with weak convergence in the same niche. Experimental studies on several well-known benchmark problems show that the proposed algorithm is competitive compared with six state-of-the-art many-objective algorithms. Moreover, the proposed algorithm has also been verified to be scalable to deal with constrained many-objective optimization problems.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Zhou, J., Zou, J., Yang, S., Zheng, J., Gong, D. and Pei, T. (2021) Niche-based and angle-based selection strategies for many-objective evolutionary optimization. Information Sciences, 571, pp. 133-153
ISSN : 0020-0255
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes