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A New Multi-objective Model for Constrained Optimisation

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Advances in Computational Intelligence Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 513))

Abstract

Multi-objective optimization evolutionary algorithms have becoming a promising approach for solving constrained optimization problems in the last decade. Standard two-objective schemes aim at minimising the objective function and the degrees of violating constraints (the degrees of violating each constraint or their sum) simultaneously. This paper proposes a new multi-objective model for constrained optimization. The model keeps the standard objectives: the original objective function and the sum of the degrees of constraint violation. Besides them, other helper objectives are constructed, which are inspired from MOEA/D or Tchebycheff method for multi-objective optimization. The new helper objectives are weighted sums of the normalized original objective function and normalized degrees of constraint violation. The normalization is a major improvement. Unlike our previous model without the normalization, experimental results demonstrate that the new model is completely superior to the standard model with two objectives. This confirms our expectation that adding more help objectives may improve the solution quality significantly.

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Acknowledgments

This work was partially supported by EPSRC under Grant No. EP/I009809/1.

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Authors and Affiliations

  1. Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK

    Tao Xu, Jun He & Changjing Shang

  2. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Weiqin Ying

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  1. Tao Xu
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  2. Jun He
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  3. Changjing Shang
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  4. Weiqin Ying
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Corresponding author

Correspondence to Tao Xu .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University Bailrigg School of Computing and Communications, Lancaster, United Kingdom

    Plamen Angelov

  2. School of Computing, University of Portsmouth School of Computing, Portsmouth, Hampshire, United Kingdom

    Alexander Gegov

  3. School of Comp. Sci. & Digital Media, Robert Gordon University School of Comp. Sci. & Digital Media, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Ins. of Mathematics, Physics & Comp. Sci, Aberystwyth University Ins. of Mathematics, Physics & Comp. Sci, Aberystwyth, United Kingdom

    Qiang Shen

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Xu, T., He, J., Shang, C., Ying, W. (2017). A New Multi-objective Model for Constrained Optimisation. In: Angelov, P., Gegov, A., Jayne, C., Shen, Q. (eds) Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-46562-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-46562-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46561-6

  • Online ISBN: 978-3-319-46562-3

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