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A Cone Decomposition Many-Objective Evolutionary Algorithm with Adaptive Direction Penalized Distance

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Bio-inspired Computing: Theories and Applications (BIC-TA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 951))

Abstract

The effectiveness of most of the existing decomposition-based multi-objective evolutionary algorithms (MOEAs) is yet to be heightened for many-objective optimization problems (MaOPs). In this paper, a cone decomposition evolutionary algorithm (CDEA) is proposed to extend decomposition-based MOEAs to MaOPs more effectively. In CDEA, a cone decomposition strategy is introduced to overcome potential troubles in decomposition-based MOEAs by decomposing a MaOP into several subproblems and associating each of them with a unique cone subregion. Then, a scalarization approach of adaptive direction penalized distance is designed to emphasize boundary subproblems and guarantee the full spread of the final obtained front. The proposed algorithm is compared with three decomposition-based MOEAs on unconstrained benchmark MaOPs with 5 to 10 objectives. Empirical results demonstrate the superior solution quality of CDEA.

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Acknowledgments

This work was supported partially by the Natural Science Foundation of Guangdong Province, China, under Grants 2015A030313204, 2017A030310013, and 2018A030313389, in part by the Fundamental Research Funds for the Central Universities, SCUT, under Grant 2017MS043, in part by the Pearl River S&T Nova Program of Guangzhou under Grant 2014J2200052, in part by the National Natural Science Foundation of China under Grants 61203310 and 61503087, in part by the Major Research and Development Program for Industrial Technology of Guangzhou City under Grant 201802010025, and in part by the Platform Development Program for Innovation and Entrepreneurship at Colleges in Guangzhou under Grant 2019PT103.

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Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Weiqin Ying, Yali Deng, Yuehong Xie & Zhenyu Wang

  2. School of Computer Science and Educational Software, Guangzhou University, Guangzhou, 510006, China

    Yu Wu

  3. School of Computers, Guangdong University of Technology, Guangzhou, 510006, China

    Zhiyi Lin

Authors
  1. Weiqin Ying
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  2. Yali Deng
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  3. Yu Wu
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  4. Yuehong Xie
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  5. Zhenyu Wang
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  6. Zhiyi Lin
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Corresponding author

Correspondence to Weiqin Ying .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Jianyong Qiao

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xinchao Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Beijing University of Posts and Telecommunications, Beijing, China

    Xingquan Zuo

  5. Anhui University, Hefei, China

    Xingyi Zhang

  6. City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Shanguo Huang

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Ying, W., Deng, Y., Wu, Y., Xie, Y., Wang, Z., Lin, Z. (2018). A Cone Decomposition Many-Objective Evolutionary Algorithm with Adaptive Direction Penalized Distance. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_34

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  • DOI: https://doi.org/10.1007/978-981-13-2826-8_34

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2825-1

  • Online ISBN: 978-981-13-2826-8

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