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Fusion of Many-Objective Non-dominated Solutions Using Reference Points

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Evolutionary Multi-Criterion Optimization (EMO 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10173))

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Abstract

With recent advancements of multi- or many-objective optimization algorithms, researchers and decision-makers are increasingly faced with the dilemma of choosing the best algorithm to solve their problems. In this paper, we propose a simple hybridization of population-based multi- or many-objective optimization algorithms called fusion of non-dominated fronts using reference points (FNFR) to gain combined benefits of several algorithms. FNFR combines solutions from multiple optimization algorithms during or after several runs and extracts well-distributed solutions from a large set of non-dominated solutions using predefined structured reference points or user-defined reference points. The proposed FNFR is applied to non-dominated solutions obtained by the Generalized Differential Evolution Generation 3 (GDE3), Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO), and the Strength Pareto Evolutionary Algorithm 2 (SPEA2) on seven unconstrained many-objective test problems with three to ten objectives. Experimental results show FNFR is an effective way for combining and extracting (fusion) of well-distributed non-dominated solutions among a large set of solutions. In fact, the proposed method is a solution-level hybridization approach. FNFR showed promising results when selecting well-distributed solutions around a specific region of interest.

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Author information

Authors and Affiliations

  1. Faculty of Electrical Computer and Software Engineering, University of Ontario Institute of Technology, Oshawa, Canada

    Amin Ibrahim & Shahryar Rahnamayan

  2. Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, Canada

    Miguel Vargas Martin

  3. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, USA

    Kalyanmoy Deb

Authors
  1. Amin Ibrahim
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  2. Shahryar Rahnamayan
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  3. Miguel Vargas Martin
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  4. Kalyanmoy Deb
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Corresponding author

Correspondence to Amin Ibrahim .

Editor information

Editors and Affiliations

  1. University of Münster, Münster, Germany

    Heike Trautmann

  2. TU Dortmund University, Dortmund, Germany

    Günter Rudolph

  3. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  4. CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

  5. Clemson University, Clemson, South Carolina, USA

    Margaret Wiecek

  6. University of Surrey, Guildford, United Kingdom

    Yaochu Jin

  7. University of Münster, Münster, Germany

    Christian Grimme

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Ibrahim, A., Rahnamayan, S., Martin, M.V., Deb, K. (2017). Fusion of Many-Objective Non-dominated Solutions Using Reference Points. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_22

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  • DOI: https://doi.org/10.1007/978-3-319-54157-0_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54156-3

  • Online ISBN: 978-3-319-54157-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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