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Deconstructing Multi-objective Evolutionary Algorithms: An Iterative Analysis on the Permutation Flow-Shop Problem

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Learning and Intelligent Optimization (LION 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8426))

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Abstract

Many studies in the literature have applied multi-objective evolutionary algorithms (MOEAs) to multi-objective combinatorial optimization problems. Few of them analyze the actual contribution of the basic algorithmic components of MOEAs. These components include the underlying EA structure, the fitness and diversity operators, and their policy for maintaining the population. In this paper, we compare seven MOEAs from the literature on three bi-objective and one tri-objective variants of the permutation flowshop problem. The overall best and worst performing MOEAs are then used for an iterative analysis, where each of the main components of these algorithms is analyzed to determine their contribution to the algorithms’ performance. Results confirm some previous knowledge on MOEAs, but also provide new insights. Concretely, some components only work well when simultaneously used. Furthermore, a new best-performing algorithm was discovered for one of the problem variants by replacing the diversity component of the best performing algorithm (NSGA-II) with the diversity component from PAES.

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Acknowledgments

The research leading to the results presented in this paper has received funding from the Meta-X ARC project, the COMEX project within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office, and the FRFC project “Méthodes de recherche hybrides pour la résolution de problèmes complexes”. Leonardo C. T. Bezerra, Manuel López-Ibáñez and Thomas Stützle acknowledge support from the Belgian F.R.S.-FNRS, of which they are a FRIA doctoral fellow, a postdoctoral researcher and a senior research associate, respectively.

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Authors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles (ULB), Brussels, Belgium

    Leonardo C. T. Bezerra, Manuel López-Ibáñez & Thomas Stützle

Authors
  1. Leonardo C. T. Bezerra
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  2. Manuel López-Ibáñez
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  3. Thomas Stützle
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Corresponding author

Correspondence to Leonardo C. T. Bezerra .

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Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. AT&T Labs Research, Middletown, New Jersey, USA

    Mauricio G.C. Resende

  3. University of Florida, Gainesville, Florida, USA

    Chrysafis Vogiatzis

  4. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Jose L. Walteros

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Bezerra, L.C.T., López-Ibáñez, M., Stützle, T. (2014). Deconstructing Multi-objective Evolutionary Algorithms: An Iterative Analysis on the Permutation Flow-Shop Problem. In: Pardalos, P., Resende, M., Vogiatzis, C., Walteros, J. (eds) Learning and Intelligent Optimization. LION 2014. Lecture Notes in Computer Science(), vol 8426. Springer, Cham. https://doi.org/10.1007/978-3-319-09584-4_16

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  • DOI: https://doi.org/10.1007/978-3-319-09584-4_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09583-7

  • Online ISBN: 978-3-319-09584-4

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