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A Cluster-Based Orthogonal Multi-Objective Genetic Algorithm

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Computational Intelligence and Intelligent Systems (ISICA 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 51))

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Abstract

Multi-objective genetic algorithm is proved to be suitable for solving multi-objective optimization problems. However, it is usually very hard to balance the convergence and diversity of a multi-objective genetic algorithm. This paper introduces a new algorithm, with both good convergence and diversity based on clustering method and multi-parent crossover operator. Meanwhile, an initial population is generated by orthogonal design to enhance the search effort of the algorithm. The experimental results on a number of test problems indicate the good performance of the Cluster-Based Orthogonal Multi-Objective Genetic Algorithm.

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Author information

Authors and Affiliations

  1. Computer Science and Technology, China Unversity of Geosciences, Wuhan, China

    Jiankai Zhu, Guangming Dai & Li Mo

Authors
  1. Jiankai Zhu
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  2. Guangming Dai
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  3. Li Mo
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Editor information

Editors and Affiliations

  1. School of Computer Science, China University of Geosciences, 430074, Wu-Han, China

    Zhihua Cai

  2. State Key Laboratory of Novel Software Technology,, Nanjing University, P.O. Box, China

    Zhenhua Li

  3. Computation Center, Wuhan University, Wuhan, China

    Zhuo Kang

  4. School of Computer Science and Engineering, The University of Aizu, Japan

    Yong Liu

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© 2009 Springer-Verlag Berlin Heidelberg

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Zhu, J., Dai, G., Mo, L. (2009). A Cluster-Based Orthogonal Multi-Objective Genetic Algorithm. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2009. Communications in Computer and Information Science, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04962-0_6

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  • DOI: https://doi.org/10.1007/978-3-642-04962-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04961-3

  • Online ISBN: 978-3-642-04962-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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