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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2013: Intelligent Data Engineering and Automated Learning – IDEAL 2013 pp 210–217Cite as

A Direction based Multi-Objective Agent Genetic Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

Abstract

A direction based multi-objective agent genetic algorithm (DMOAGA) is proposed in this paper. In order to take advantage of the effective direction information and depth of local search to mine non-dominated solutions, the direction perturbation operator is also employed. The neighborhood non-dominated solutions are generated using tournament selection and “average distance” rule, which maintains the diversity of non-dominated solution set. In the experiments, the benchmark problems UF1~UF6 and ZDT1~ZDT4 are used to validate the performance of DMOAGA. We compared it with NSGA-II and DMEA in terms of generational distance (GD) and inverted generational distance (IGD). The results show that DMOAGA has a good diversity and convergence, the performances on most of benchmark problems are better than DMEA and NSGA-II.

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Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China

    Chen Zhu & Jing Liu

Authors
  1. Chen Zhu
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  2. Jing Liu
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  2. University of Science and Technology of China, Hefei, China

    Ke Tang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. Ostfalia University of Applied Sciences, 38302, Wolfenbüttel, Germany

    Frank Klawonn

  5. Kyungpook National University, 702-701, Buk-Gu, Daegu, Korea

    Minho Lee

  6. Nature Inspired Computational and Applications Laboratory, School of Computer Science and Technology,, University of Science and Technology of China, 230027, Hefei, China

    Thomas Weise

  7. University of Science and Technology of China, 230017, Hefei, China

    Bin Li

  8. CERCIA, School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Xin Yao

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© 2013 Springer-Verlag Berlin Heidelberg

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Zhu, C., Liu, J. (2013). A Direction based Multi-Objective Agent Genetic Algorithm. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_26

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  • DOI: https://doi.org/10.1007/978-3-642-41278-3_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41277-6

  • Online ISBN: 978-3-642-41278-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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