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Many-objective E-dominance dynamical evolutionary algorithm based on adaptive grid

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Abstract

In evolutionary multi-objective optimization, achieving a balance between convergence speed and population diversity remains a challenging topic especially for many-objective optimization problems (MaOPs). To accelerate convergence toward the Pareto front and maintain a high degree of diversity for MaOPs, we propose a new many-objective dynamical evolutionary algorithm based on E-dominance and adaptive-grid strategies (EDAGEA). In EDAGEA, it incorporates the E_dominance and adaptive strategies to enhance the search ability. Instead of the Pareto dominance mechanism in the traditional dynamical evolutionary algorithm, EDAGEA employs the E-dominance strategy to improve the selective pressure and to accelerate the convergence speed. Moreover, EDAGEA incorporates the adaptive-grid strategy to promote the uniformity and diversity of the population. In the experiments, the proposed EDAGEA algorithm is tested on DTLZ series problems under 3–8 objectives with diverse characteristics and is compared with two excellent many-objective evolutionary algorithms. Experimental results demonstrate that the proposed EDAGEA algorithm exhibits competitive performance in terms of both convergence speed and diversity of population.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Nos. 41561091, 41261093, 61364015 and 61462036), by the Natural Science Foundation of Jiangxi, China (Nos. 20151BAB217010 and 20151BAB201015).

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Authors and Affiliations

  1. School of Science, Jiangxi University of Science and Technology, Ganzhou, 341000, China

    Xuezhi Yue, Zhaolu Guo & Yali Yin

  2. School of Architectural and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China

    Xiaosheng Liu

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  1. Xuezhi Yue
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  2. Zhaolu Guo
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  3. Yali Yin
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  4. Xiaosheng Liu
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Correspondence to Xuezhi Yue.

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The authors declare that there is no conflict of interests regarding the publication of this paper.

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Communicated by V. Loia.

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Yue, X., Guo, Z., Yin, Y. et al. Many-objective E-dominance dynamical evolutionary algorithm based on adaptive grid. Soft Comput 22, 137–146 (2018). https://doi.org/10.1007/s00500-016-2314-8

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