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MH-MOEA: A New Multi-Objective Evolutionary Algorithm Based on the Maximin Fitness Function and the Hypervolume Indicator

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Parallel Problem Solving from Nature – PPSN XIII (PPSN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8672))

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Abstract

In this paper, we propose an approach that combines a modified version of the maximin fitness function and the hypervolume indicator for selecting individuals into a Multi-Objective Evolutionary Algorithm (MOEA). Our proposed selection mechanism is incorporated into a MOEA which adopts the crossover and mutation operators of the Nondominated Sorting Genetic Algorithm-II (NSGA-II), giving rise to the so-called “Maximin-Hypervolume Multi-Objective Evolutionary Algorithm (MH-MOEA)”. Our proposed MH-MOEA is validated using standard test problems taken from the specialized literature, using from three to six objectives. Our results are compared with respect to those produced by MC-MOEA (which is based on the maximin fitness function and a clustering technique), MOEA/D using Penalty Boundary Intersection (PBI), which is based on decomposition and iSMS-EMOA (which is based on the hypervolume indicator). Our preliminary results indicate that our proposed MH-MOEA is a good alternative to solve multi-objective optimization problems having both low dimensionality and high dimensionality in objective function space.

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Authors and Affiliations

  1. Departamento de Computación, CINVESTAV-IPN (Evolutionary Computation Group), México D.F., 07300, México

    Adriana Menchaca-Mendez & Carlos A. Coello Coello

Authors
  1. Adriana Menchaca-Mendez
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  2. Carlos A. Coello Coello
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Editor information

Editors and Affiliations

  1. Faculty of Computer and Engineering Sciences, Cologne University of Applied Sciences, Steinmüllerallee 1, 51643, Gummersbach, Germany

    Thomas Bartz-Beielstein

  2. Warwick Business School, University of Warwick, CV8 2SY, Coventry, UK

    Jürgen Branke

  3. Department of Intelligent Systems, JožefStefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia

    Bogdan Filipič

  4. Department of Computer Science and Creative Technologies, University of the West of England, BS16 1QY, Bristol, UK

    Jim Smith

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© 2014 Springer International Publishing Switzerland

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Menchaca-Mendez, A., Coello Coello, C.A. (2014). MH-MOEA: A New Multi-Objective Evolutionary Algorithm Based on the Maximin Fitness Function and the Hypervolume Indicator. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_64

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  • DOI: https://doi.org/10.1007/978-3-319-10762-2_64

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10761-5

  • Online ISBN: 978-3-319-10762-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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