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An Improved Bi-goal Algorithm for Many-Objective Optimization

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Book cover E-Learning and Games (Edutainment 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11462))

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Abstract

In this paper, an evolutionary algorithm based on bi-goal is proposed for many-objective optimization. We first provide a new proximity estimation, ensuring the convergence of algorithm. Afterwards, a new sharing function with a novel discriminator is employed to improve the diversity. The dominance-based environmental selection is applied in bi-goal space, which is expected to archive a good balance between convergence and diversity. The experimental results show that the proposed method can work well on most instances considered in this study, demonstrating that it is very competitive for solving many-objective optimization problems.

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Author information

Authors and Affiliations

  1. Xing Zhi College of Xi’an University of Finance and Economics, NO. 57 Dizhai Road, Xi’an, 710038, Shaanxi, China

    Huaxian Pan

  2. Faculty of Computer Science and Engineering, Xi’an University of Technology, NO. 5 South Jinhua Road, Xi’an, 710048, Shaanxi, China

    Lei Cai

Authors
  1. Huaxian Pan
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  2. Lei Cai
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Corresponding author

Correspondence to Lei Cai .

Editor information

Editors and Affiliations

  1. Liverpool John Moores University, Liverpool, UK

    Abdennour El Rhalibi

  2. Hangzhou Normal University, Hangzhou, China

    Zhigeng Pan

  3. Xi’an University of Technology, Xi’an, China

    Haiyan Jin

  4. Hangzhou Normal University, Hangzhou, China

    Dandan Ding

  5. Pontificia Universidad Javeriana, Cali, Colombia

    Andres A. Navarro-Newball

  6. Xi’an University of Technology, Xi’an, China

    Yinghui Wang

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Pan, H., Cai, L. (2019). An Improved Bi-goal Algorithm for Many-Objective Optimization. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_28

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  • DOI: https://doi.org/10.1007/978-3-030-23712-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23711-0

  • Online ISBN: 978-3-030-23712-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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