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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2019: Evolutionary Multi-Criterion Optimization pp 399–410Cite as

Linear Search Mechanism for Multi- and Many-Objective Optimisation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11411))

Abstract

This article proposes a search mechanism based on linear combinations of population members to increase the solution quality of multi-objective and many-objective optimisation algorithms. Our approach makes use of the inherent knowledge in the solution population at a given time step, and forms new solutions through linear combinations of the existing ones. A population of coefficient vectors is formed and optimised by a metaheuristic to explore and exploit promising areas of the search space. In addition, our proposed method provides a reduction of dimensionality for large search spaces. The concept is formally introduced and implemented into a generic algorithm structure to be used in arbitrary metaheuristics. The experimental evaluation uses four multi- and many-objective algorithms (NSGA-II, GDE3, NSGA-III and RVEA) and is performed on a total of 60 test instances from three benchmark families with 2 to 5 objective functions and 30 to 514 decision variables. The results indicate that the performance of existing methods can be significantly improved by the proposed search strategy, especially in high-dimensional search spaces and for many-objective problems.

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Authors and Affiliations

  1. Institute for Intelligent Cooperative Systems, Otto von Guericke University, Magdeburg, Germany

    Heiner Zille & Sanaz Mostaghim

Authors
  1. Heiner Zille
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  2. Sanaz Mostaghim
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Corresponding author

Correspondence to Heiner Zille .

Editor information

Editors and Affiliations

  1. Michigan State University, East Lansing, MI, USA

    Kalyanmoy Deb

  2. Michigan State University, East Lansing, MI, USA

    Erik Goodman

  3. CINVESTAV-IPN, Mexico, Mexico

    Carlos A. Coello Coello

  4. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  5. University of Jyvaskyla, Jyväskylä, Finland

    Kaisa Miettinen

  6. Otto von Guericke University Magdeburg, Magdeburg, Germany

    Sanaz Mostaghim

  7. Cornell University, Ithaca, NY, USA

    Patrick Reed

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Zille, H., Mostaghim, S. (2019). Linear Search Mechanism for Multi- and Many-Objective Optimisation. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_32

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  • DOI: https://doi.org/10.1007/978-3-030-12598-1_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12597-4

  • Online ISBN: 978-3-030-12598-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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