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Constraint Method-Based Evolutionary Algorithm (CMEA) for Multiobjective Optimization

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Book cover Evolutionary Multi-Criterion Optimization (EMO 2001)

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

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Abstract

Evolutionary algorithms are becoming increasingly valuable in solving large-scale, realistic engineering multiobjective optimization (MO) problems, which typically require consideration of conflicting and competing design issues. The new procedure, Constraint Method-Based Evolutionary Algorithm (CMEA), presented in this paper is based upon underlying concepts in the constraint method described in the mathematical programming literature. Pareto optimality is achieved implicitly via a constraint approach, and convergence is enhanced by using beneficial seeding of the initial population. CMEA is evaluated by solving two test problems reported in the multiobjective evolutionary algorithm (MOEA) literature. Performance comparisons based on quantitative metrics for accuracy, coverage, and spread are presented. CMEA is relatively simple to implement and incorporate into existing implementations of evolutionary algorithm-based optimization procedures.

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, North Carolina State University, NC 27695, Campus Box, 7908, Raleigh, USA

    S. Ranji Ranjithan

  2. Siebel Systems, 6001 Shellmound, 94608, Emeryville, CA, USA

    S. Kishan Chetan

  3. Department of Civil Engineering, North Carolina State University, NC 27695, Campus Box, 7908, Raleigh, USA

    Harish K. Dakshina

Authors
  1. S. Ranji Ranjithan
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  2. S. Kishan Chetan
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  3. Harish K. Dakshina
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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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Ranji Ranjithan, S., Kishan Chetan, S., Dakshina, H.K. (2001). Constraint Method-Based Evolutionary Algorithm (CMEA) for Multiobjective Optimization. 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_21

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  • DOI: https://doi.org/10.1007/3-540-44719-9_21

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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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