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A Decomposition-Based Evolutionary Algorithm with Adaptive Weight Vectors for Multi- and Many-objective Optimization

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Applications of Evolutionary Computation (EvoApplications 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12104))

Abstract

The multi-objective evolutionary algorithms based on decomposition (MOEA/D) have achieved great success in the area of evolutionary multi-objective optimization. Numerous MOEA/D variants are focused on solving the normalized multi- and many-objective problems without paying attention to problems having objectives with different scales. For this purpose, this paper proposes a decomposition-based evolutionary algorithm with adaptive weight vectors (DBEA-AWV) for both the normalized and scaled multi- and many-objective problems. In the light of this direction, we compare existing popular decomposition approaches and choose the best suitable one incorporated into DBEA-AWV. Moreover, one novel replacement strategy is adopted to attain the balance between convergence and diversity for multi- and many-objective optimization problems. Our experimental results demonstrate that the proposed algorithm is efficient and reliable for dealing with different normalized and scaled problems, outperforming several other state-of-the-art multi- and many-objective evolutionary algorithms.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Free University of Berlin, Berlin, Germany

    Guang Peng & Katinka Wolter

Authors
  1. Guang Peng
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  2. Katinka Wolter
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Corresponding author

Correspondence to Guang Peng .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

  3. Universidad de Extremadura, Mérida, Spain

    Francisco Fernández de Vega

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Peng, G., Wolter, K. (2020). A Decomposition-Based Evolutionary Algorithm with Adaptive Weight Vectors for Multi- and Many-objective Optimization. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science(), vol 12104. Springer, Cham. https://doi.org/10.1007/978-3-030-43722-0_10

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  • DOI: https://doi.org/10.1007/978-3-030-43722-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43721-3

  • Online ISBN: 978-3-030-43722-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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