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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2001: Evolutionary Multi-Criterion Optimization pp 226–240Cite as

A Statistical Comparison of Multiobjective Evolutionary Algorithms Including the MOMGA-II

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1993))

Abstract

Many real-world scientific and engineering applications involve finding innovative solutions to “hard” Multiobjective Optimization Problems (MOP). Various Multiobjective Evolutionary Algorithms (MOEA) have been developed to obtain MOP Pareto solutions. A particular exciting MOEA is the MOMGA which is an extension of the single-objective building block (BB) based messy Genetic Algorithm. The intent of this discussion is to illustrate that modifications made to the Multi-Objective messy GA (MOMGA) have further improved its efficiency resulting in the MOMGA-II. The MOMGA-II uses a probabilistic BB approach to initializing the population referred to as Probabilistically Complete Initialization. This has the effect of improving the efficiency of the MOMGA through the reduction of computational bottle-necks. Similar statistical results have been obtained using the MOMGA-II as compared to the results of the original MOMGA as well as those obtained by other MOEAs as tested with standard generic MOP test suites.

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Author information

Authors and Affiliations

  1. Dept of Electrical and Computer Engineering, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA

    Jesse B. Zydallis & Gary B. Lamont

  2. Optical Radiation Branch, Air Force Research Laboratory, Brooks AFB, 78235, TX, USA

    David A. Van Veldhuizen

Authors
  1. Jesse B. Zydallis
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  2. David A. Van Veldhuizen
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  3. Gary B. Lamont
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, ETH Zentrum, Gloriastrasse 35, 8092, Zurich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  2. Department of Mechanical Engineering Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, UP 208 016, Kanpur, India

    Kalyanmoy Deb

  3. Electrical Engineering Department,Computer Science Section Av.Instituto Politecnico Nacional No.2508, CINVESTAV-IPN, Col.San Pedro Zacatenco, 07300, Mexico City, Mexico

    Carlos Artemio Coello Coello

  4. Department of Computer Science, University of Reading, P.O.Box 225, Reading RG6 6AY, Whiteknights, UK

    David Corne

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© 2001 Springer-Verlag Berlin Heidelberg

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Zydallis, J.B., Van Veldhuizen, D.A., Lamont, G.B. (2001). A Statistical Comparison of Multiobjective Evolutionary Algorithms Including the MOMGA-II. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_16

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  • DOI: https://doi.org/10.1007/3-540-44719-9_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

  • eBook Packages: Springer Book Archive

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