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International Conference on Swarm Intelligence

ICSI 2016: Advances in Swarm Intelligence pp 599–605Cite as

Objective Space Partitioning with a Novel Conflict Information Measure for Many-Objective Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9712))

Abstract

In this paper, we present a novel conflict information measure used for objective space partitioning in solving many-objective optimization problems. Obtained from the current Pareto front approximation to estimate the degree of conflict between objectives, the conflict information is simply evaluated by counting the occurrence of conflict (improvement vs deterioration) out of all decision making sample pairs. The proposed method is compared with the latest objective space partitioning based on Pearson correlation coefficient conflict information. The results show that the proposed method outperforms the comparison method on identifying the conflicting objectives.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 61171124, 61301298) and Shenzhen Key project for Foundation Research (JC201105170613A).

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Authors and Affiliations

  1. College of Information Engineering, Shenzhen University, Shenzhen, 518060, China

    Naili Luo, Jianping Luo & Xia Li

  2. Shenzhen Key Lab of Communication and Information Processing, Shenzhen, 518060, China

    Naili Luo, Jianping Luo & Xia Li

Authors
  1. Naili Luo
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  2. Jianping Luo
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  3. Xia Li
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Corresponding author

Correspondence to Xia Li .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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© 2016 Springer International Publishing Switzerland

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Luo, N., Luo, J., Li, X. (2016). Objective Space Partitioning with a Novel Conflict Information Measure for Many-Objective Optimization. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_60

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  • DOI: https://doi.org/10.1007/978-3-319-41000-5_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40999-3

  • Online ISBN: 978-3-319-41000-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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