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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2014: Simulated Evolution and Learning pp 192–203Cite as

A Novel Hybrid Multi-objective Optimization Framework: Rotating the Objective Space

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

Abstract

Multi-objective Evolutionary Algorithms (MOEAs) are popular approaches for solving multi-objective problems (MOPs). One representative method is Non-dominated Sorting Genetic Algorithm II (NSGA-II), which has achieved great success in the field by introducing non-dominated sorting into survival selection. However, as a common issue for dominance-based algorithms, the performance of NSGA-II will decline in solving problems with 3 or more objectives. This paper aims to circumvent this issue by incorporating the concept of decomposition into NSGA-II. A grouping-based hybrid multi-objective optimization framework is proposed for tackling 3-objective problems. Original MOP is decomposed into several scalar subproblems, and each group of population is assigned with two scalar subproblems as new objectives. In order to better cover the whole objective space, new objective spaces are formulated via rotating the original objective space. Simulation results show that the performance of the proposed algorithm is competitive when dealing with 3-objective problems.

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Author information

Authors and Affiliations

  1. NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore

    Xin Qiu

  2. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore

    Ye Huang, Jian-Xin Xu & Kay Chen Tan

Authors
  1. Xin Qiu
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  2. Ye Huang
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  3. Jian-Xin Xu
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  4. Kay Chen Tan
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Cite this paper

Qiu, X., Huang, Y., Xu, JX., Tan, K.C. (2014). A Novel Hybrid Multi-objective Optimization Framework: Rotating the Objective Space. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_17

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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