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An Extension of Generalized Differential Evolution for Multi-objective Optimization with Constraints

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Parallel Problem Solving from Nature - PPSN VIII (PPSN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3242))

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Abstract

In this paper an extension of Generalized Differential Evolution for constrained multi-objective (Pareto-)optimization is proposed. The proposed extension adds a mechanism for maintaining extent and distribution of the obtained non-dominated solutions approximating a Pareto front. The proposed extension is tested with a set of five benchmark multi-objective test problems and results are numerically compared to known global Pareto fronts and to results obtained with the elitist Non-Dominated Sorting Genetic Algorithm and Generalized Differential Evolution. Results show that the extension improves extent and distribution of solutions of Generalized Differential Evolution.

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References

  1. Chang, C.S., Xu, D.Y., Quek, H.B.: Pareto-optimal set based multiobjective tuning of fuzzy automatic train operation for mass transit system. IEE Proceedings on Electric Power Applications 146, 577–583 (1999)

    Article  Google Scholar 

  2. Abbass, H.A., Sarker, R.: The Pareto Differential Evolution algorithm. International Journal on Artificial Intelligence Tools 11, 531–552 (2002)

    Article  Google Scholar 

  3. Madavan, N.K.: Multiobjective optimization using a Pareto Differential Evolution approach. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, Honolulu, Hawaii, pp. 1145–1150 (2002)

    Google Scholar 

  4. Babu, B.V., Jehan, M.M.L.: Differential Evolution for multi-objective optimization. In: Proceedings of the 2003 Congress on Evolutionary Computation, CEC 2003, Canberra, Australia, pp. 2696–2703 (2003)

    Google Scholar 

  5. Feng Xue, A.C., Sanderson, R.J.G.: Multi-objective differential evolution and its application to enterprise planning. In: Proceedings of IEEE International Conference on Robotics and Automation, Taiwan, pp. 3535–3541 (2003)

    Google Scholar 

  6. Pareto, V.: Cours D’Economie Politique. Libraire Droz, Geneve (1964) (the first edition in 1896)

    Google Scholar 

  7. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston (1998)

    Google Scholar 

  8. Storn, R., Price, K.V.: Differential Evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  9. Lampinen, J., Storn, R.: Differential Evolution. In: New Optimization Techniques in Engineering, pp. 123–166. Springer, Heidelberg (2004)

    Google Scholar 

  10. Corne, D., Dorigo, M., Glover, F.: New Ideas in Optimization. McGraw-Hill, London (1999)

    Google Scholar 

  11. Storn, R., Price, K.V.: Differential Evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, ICSI, [Online] Available (1995), http://ftp.icsi.berkeley.edu/pub/techreports/1995/tr-95-012.ps.gz (3.5.2004)

  12. Lampinen, J.: A constraint handling approach for the Differential Evolution algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, Honolulu, Hawaii, pp. 1468–1473 (2002)

    Google Scholar 

  13. Lampinen, J.: DE’s selection rule for multiobjective optimization. Technical report, Lappeenranta University of Technology, Department of Information Technology (2001), [Online] Available: http://www.it.lut.fi/kurssit/03-04/010778000/MODE.pdf (3.5.2004)

  14. Lampinen, J.: Multi-constrained nonlinear optimization by the Differential Evolution algorithm. Technical report, Lappeenranta University of Technology, Department of Information Technology (2001), [Online] Available: http://www.it.lut.fi/kurssit/03-04/010778000/DECONSTR.PDF (3.5.2004)

  15. Kukkonen, S., Lampinen, J.: A Differential Evolution algorithm for constrained multi-objective optimization: Initial assessment. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, Innsbruck, Austria, pp. 96–102 (2004)

    Google Scholar 

  16. Kukkonen, S., Lampinen, J.: Mechanical component design for multiple objectives using Generalized Differential Evolution. In: Proceedings of the 6th International Conference on Adaptive Computing in Design and Manufacture (ACDM 2004), Bristol, United Kingdom, pp. 261–272 (2004)

    Google Scholar 

  17. Kukkonen, S., Lampinen, J.: Comparison of Generalized Differential Evolution algorithm to other multi-objective evolutionary algorithms. In: Proceedings of the 4th European Congress on Computational Methods in Applied Sciences and Engineering, Jyväskylä, Finland (2004) (Accepted for publication)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  19. Deb, K.: Multi-Objective Optimization using Evolutionary algorithms. JohnWiley & Sons, Chichester (2001)

    MATH  Google Scholar 

  20. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8, 173–195 (2000), Also available: ftp.tik.ee.ethz.ch/pub/people/zitzler/ZDT2000.ps (15.1.2004)

    Article  Google Scholar 

  21. Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Swiss Federal Institute of Technology (ETH) Zurich, TIK-Schriftenreihe Nr. 30, Diss ETH No. 13398, Shaker Verlag, Germany (1999)

    Google Scholar 

  22. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach. IEEE Transactions on Evolutionary Computation 4, 257–271 (1999)

    Article  Google Scholar 

  23. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Technology, Lappeenranta University of Technology, P.O. Box 20, FIN-53851, Lappeenranta, Finland

    Saku Kukkonen & Jouni Lampinen

Authors
  1. Saku Kukkonen
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  2. Jouni Lampinen
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Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Xin Yao

  2. Automated Scheduling, Optimisation and Planning Group, School of Computer Science & IT, University of Nottingham, NG8 1BB, Nottingham, UK

    Edmund K. Burke

  3. Intelligent Systems Group Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizábal 1, 20018, San Sebastian, Donostia, Spain

    José A. Lozano

  4. University of the West of England, Bristol, United Kingdom

    Jim Smith

  5. GeNeura Team, Depto. Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain

    Juan Julián Merelo-Guervós

  6. School of Computer Science, The University of Birmingham, B15 1TT, Edgbaston, Birmingham, UK

    John A. Bullinaria

  7. School of Computer Science, University of Birmingham, B15 2TT, Birmingham, Great Britain

    Jonathan E. Rowe

  8. School of Computer Science, University of Birmingham, United Kingdom

    Peter Tiňo

  9. School of Computer Science, The University of Birmingham, B15 2TT, Birmingham, UK

    Ata Kabán

  10. Faculty of Computer Science, Dortmund University of Technology, 44221, Dortmund, Germany

    Hans-Paul Schwefel

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© 2004 Springer-Verlag Berlin Heidelberg

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Kukkonen, S., Lampinen, J. (2004). An Extension of Generalized Differential Evolution for Multi-objective Optimization with Constraints. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_76

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  • DOI: https://doi.org/10.1007/978-3-540-30217-9_76

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23092-2

  • Online ISBN: 978-3-540-30217-9

  • eBook Packages: Springer Book Archive

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