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Evolutionary many-objective optimization algorithm based on angle and clustering

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Abstract

In evolutionary multi-objective optimization, maintaining a well balance of convergence and diversity is particularly important for the performance of evolutionary algorithms. Considering the convergence and diversity at the same time, a many-objective optimization algorithm combining angle-based selection strategy and clustering strategy is proposed. In the former strategy, the whole population is divided into several partitions to ensure the diversity of the population, and superior individuals are selected to ensure the convergence of the population. The latter strategy, the individual vector angle is used to reflect the similarity and the individuals are divided into some clusters, which helps to describe the population distribution. The performance of this algorithm is compared with five state-of-the-art evolutionary many-objective optimization algorithms on a variety of benchmark test problems with 5, 10 and 15 objectives. The results suggest that the algorithm can slightly better competitive performance.

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Acknowledgements

This work was supported by Natural Science Foundation-Steel and Iron Foundation of Hebei Province (Grant Nos. E2019105123), Department of Education of Hebei Province (Grant Nos. ZD2019311). The authors would like to thank the editor and anonymous reviewers for their helpful comments and suggestions to improve the quality of this paper.

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Authors and Affiliations

  1. School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, People’s Republic of China

    Zhijian Xiong, Jingming Yang & Ziyu Hu

  2. Department of Computer Science and Technology, Tangshan University, Tangshan, 063000, People’s Republic of China

    Zhijian Xiong & Zhiwei Zhao

  3. Information Department, Kailuan General Hospital, Tangshan, 063000, People’s Republic of China

    Xiaojing Wang

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  1. Zhijian Xiong
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Correspondence to Zhiwei Zhao.

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Xiong, Z., Yang, J., Hu, Z. et al. Evolutionary many-objective optimization algorithm based on angle and clustering. Appl Intell 51, 2045–2062 (2021). https://doi.org/10.1007/s10489-020-01874-2

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