Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Omni-optimizer: A Procedure for Single and Multi-objective Optimization

  • Conference paper
Evolutionary Multi-Criterion Optimization (EMO 2005)

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

Included in the following conference series:

Abstract

Due to the vagaries of optimization problems encountered in practice, users resort to different algorithms for solving different optimization problems. In this paper, we suggest an optimization procedure which specializes in solving multi-objective, multi-global problems. The algorithm is carefully designed so as to degenerate to efficient algorithms for solving other simpler optimization problems, such as single-objective uni-global problems, single-objective multi-global problems and multi-objective uni-global problems. The efficacy of the proposed algorithm in solving various problems is demonstrated on a number of test problems. Because of it’s efficiency in handling different types of problems with equal ease, this algorithm should find increasing use in real-world optimization problems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Deb, K.: Genetic algorithms in multi-modal function optimization. Master’s thesis, Tuscaloosa, AL, University of Alabama (1989)

    Google Scholar 

  2. Deb, K.: Optimization for Engineering Design: Algorithms and Examples. Prentice-Hall, New Delhi (1995)

    Google Scholar 

  3. Deb, K.: An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 186(2–4), 311–338 (2000)

    Article  MATH  Google Scholar 

  4. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  5. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  6. Ehrgott, M.: Multicriteria Optimization. Springer, Berlin (2000)

    MATH  Google Scholar 

  7. Eshelman, L.J.: The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Foundations of Genetic Algorithms 1 (FOGA-1), pp. 265–283 (1991)

    Google Scholar 

  8. Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of the First International Conference on Genetic Algorithms and Their Applications, pp. 41–49 (1987)

    Google Scholar 

  9. Jensen, M.T.: Reducing the run-time complexity of multiobjective EAs. IEEE Transcations to Evolutionary Computation 7(5), 503–515 (2003)

    Article  Google Scholar 

  10. Kung, H.T., Luccio, F., Preparata, F.P.: On finding the maxima of a set of vectors. Journal of the Association for Computing Machinery 22(4), 469–476 (1975)

    MATH  MathSciNet  Google Scholar 

  11. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer, Boston (1999)

    MATH  Google Scholar 

  12. Pétrowski, A.: A clearing procedure as a niching method for genetic algorithms. In: IEEE 3rd International Conference on Evolutionary Computation (ICEC1996), pp. 798–803 (1996)

    Google Scholar 

  13. Schwefel, H.-P.: Evolution and Optimum Seeking. Wiley, New York (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology Kanpur, Kanpur, PIN 208 016, India

    Kalyanmoy Deb & Santosh Tiwari

Authors
  1. Kalyanmoy Deb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Santosh Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Deb, K., Tiwari, S. (2005). Omni-optimizer: A Procedure for Single and Multi-objective Optimization. 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_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-31880-4_4

  • 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)

Publish with us

Policies and ethics

Search

Navigation