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A new dominance relation based on convergence indicators and niching for many-objective optimization

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Abstract

Maintaining a good balance between convergence and diversity is crucial in many-objective optimization, while most existing dominance relations can not achieve a good balance between them. In this paper, we propose a new dominance relation to better balance the convergence and diversity. In the proposed dominance relation, a convergence indicator and a niching technique based adaptive parameter are adopted to ensure the convergence and diversity of the nondominated solution set. Based on the proposed dominance relation, a new many-objective evolutionary algorithm is proposed. In the algorithm, a new distribution estimation method is proposed to obtain better solutions for mating selection. Experimental results indicate that the proposed dominance relation outperforms existing dominance relations in balancing the convergence and diversity and the proposed algorithms has a competitive performance against several state-of-art many-objective evolutionary algorithms.

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Acknowledgements

This work was supported in part by the National Nature Science Foundation of China (No. 61763010), in part by the Key Technologies R & D Program of Hebei (No. 19970311D), in part by the Educational Commission of Hebei Province of China (No. ZD2018083, ZD2018043, ZD2019134) and by the Startup Foundation for PhD of Hebei GEO University (No. BQ201322).

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Authors and Affiliations

  1. School of Information Engineering, Hebei GEO University, Shijiazhuang, 050031, China

    Feng Yang, Liang Xu, Xiaokai Chu & Shenwen Wang

  2. Laboratory of Artificial Intelligence and Machine Learning, Hebei GEO University, Shijiazhuang, 050031, China

    Feng Yang, Liang Xu, Xiaokai Chu & Shenwen Wang

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  1. Feng Yang
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  2. Liang Xu
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Correspondence to Shenwen Wang.

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Yang, F., Xu, L., Chu, X. et al. A new dominance relation based on convergence indicators and niching for many-objective optimization. Appl Intell 51, 5525–5542 (2021). https://doi.org/10.1007/s10489-020-01976-x

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