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The Value of Online Adaptive Search: A Performance Comparison of NSGAII, ε-NSGAII and εMOEA

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Evolutionary Multi-Criterion Optimization (EMO 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3410))

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Abstract

This paper demonstrates how adaptive population-sizing and epsilon-dominance archiving can be combined with the Nondominated Sorted Genetic Algorithm-II (NSGAII) to enhance the algorithm’s efficiency, reliability, and ease-of-use. Four versions of the enhanced Epsilon Dominance NSGA-II (ε-NSGAII) are tested on a standard suite of evolutionary multiobjective optimization test problems. Comparative results for the four variants of the (ε-NSGAII demonstrate that adapting population size based on online changes in the epsilon dominance archive size can enhance performance. The best performing version of the (ε-NSGAII is also compared to the original NSGAII and the (εMOEA on the same suite of test problems. The performance of each algorithm is measured using three running performance metrics, two of which have been previously published, and one new metric proposed by the authors. Results of the study indicate that the new version of the NSGAII proposed in this paper demonstrates improved performance on the majority of two-objective test problems studied.

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Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, The Pennsylvania State University, 406B Sackett Building, University Park, PA, 16802-1408

    Joshua B. Kollat

  2. Department of Civil and Environmental Engineering, The Pennsylvania State University, 212 Sackett Building, University Park, PA, 16802-1408

    Patrick M. Reed

Authors
  1. Joshua B. Kollat
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  2. Patrick M. Reed
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Editor information

Editors and Affiliations

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

    Carlos A. Coello Coello

  2. Centro de Investigación en Matemáticas (CIMAT), A.P. 402, Guanajuato, 36000, Mexico

    Arturo Hernández Aguirre

  3. Computer Engineering and Networks Laboratory, ETH, Zurich, Switzerland

    Eckart Zitzler

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© 2005 Springer-Verlag Berlin Heidelberg

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Kollat, J.B., Reed, P.M. (2005). The Value of Online Adaptive Search: A Performance Comparison of NSGAII, ε-NSGAII and εMOEA. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_27

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  • DOI: https://doi.org/10.1007/978-3-540-31880-4_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24983-2

  • Online ISBN: 978-3-540-31880-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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