Skip to main content
SpringerLink
Log in

Differential Evolution versus Genetic Algorithms in Multiobjective Optimization

  • Conference paper
Evolutionary Multi-Criterion Optimization (EMO 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4403))

Included in the following conference series:

Abstract

This paper presents a comprehensive comparison between the performance of state-of-the-art genetic algorithms NSGA-II, SPEA2 and IBEA and their differential evolution based variants DEMO\(^\text{NS-II}\), DEMO\(^\text{SP2}\) and DEMO\(^\text{IB}\). Experimental results on 16 numerical multiobjective test problems show that on the majority of problems, the algorithms based on differential evolution perform significantly better than the corresponding genetic algorithms with regard to applied quality indicators. This suggests that in numerical multiobjective optimization, differential evolution explores the decision space more efficiently than genetic algorithms.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Price, K.V., Storn, R.: Differential evolution – A simple evolution strategy for fast optimization. Dr. Dobb’s Journal 22(4), 18–24 (1997)

    MathSciNet  Google Scholar 

  2. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, New York (2005)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  4. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm. In: Proceedings of Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems – EUROGEN 2001, September 2001, pp. 95–100 (2001)

    Google Scholar 

  5. Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN VIII. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)

    Google Scholar 

  6. Abbass, H.A., Sarker, R., Newton, C.: PDE: A Pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the 2001 Congress on Evolutionary Computation – CEC 2001, vol. 2, May 2001, pp. 971–978 (2001)

    Google Scholar 

  7. Lampinen, J.: DE’s selection rule for multiobjective optimization. Technical report, Lappeenranta University of Technology (2001)

    Google Scholar 

  8. Madavan, N.K.: Multiobjective optimization using a Pareto differential evolution approach. In: Proceedings of the 2002 Congress on Evolutionary Computation – CEC 2002, vol. 2, May 2002, pp. 1145–1150 (2002)

    Google Scholar 

  9. Xue, F., Sanderson, A.C., Graves, R.J.: Pareto-based multi-objective differential evolution. In: Proceedings of the 2003 Congress on Evolutionary Computation – CEC 2003, vol. 2, December 2003, pp. 862–869 (2003)

    Google Scholar 

  10. Iorio, A.W., Li, X.: Solving rotated multi-objective optimization problems using differential evolution. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 861–872. Springer, Heidelberg (2004)

    Google Scholar 

  11. Kukkonen, S., Lampinen, J.: An extension of generalized differential evolution for multi-objective optimization with constraints. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN VIII. LNCS, vol. 3242, pp. 752–761. Springer, Heidelberg (2004)

    Google Scholar 

  12. Robič, T., Filipič, B.: DEMO: Differential evolution for multiobjective optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 520–533. Springer, Heidelberg (2005)

    Google Scholar 

  13. Kukkonen, S., Lampinen, J.: GDE3: The third evolution step of generalized differential evolution. In: Proceedings of the 2005 Congress on Evolutionary Computation – CEC 2005, vol. 1, September 2005, pp. 443–450 (2005)

    Google Scholar 

  14. Iorio, A.W., Li, X.: Incorporating directional information within a differential evolution algorithm for multi-objective optimization. In: Proceedings of the 2006 Genetic and Evolutionary Computation Conference – GECCO 2006, vol. 1, July 2006, pp. 675–682 (2006)

    Google Scholar 

  15. Santana-Quintero, L.V., Coello Coello, C.A.: An algorithm based on differential evolution for multiobjective problems. In: Smart Engineering System Design: Neural Networks, Evolutionary Programming and Artificial Life, November 2005, pp. 211–220 (2005)

    Google Scholar 

  16. Hernández-Díaz, A.G., Santana-Quintero, L.V., Coello Coello, C., Caballero, R., Molina, J.: A new proposal for multi-objective optimization using differential evolution and rough sets theory. In: Proceedings of the 2006 Genetic and Evolutionary Computation Conference – GECCO 2006, vol. 1, July 2006, pp. 675–682 (2006)

    Google Scholar 

  17. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. In: Abraham, A., Jain, R., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization: Theoretical Advances and Applications, pp. 105–145. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  18. Huband, S., Barone, L., White, L., Hingston, P.: A scalable multi-objective test problem toolkit. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 280–295. Springer, Heidelberg (2005)

    Google Scholar 

  19. Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA – A platform and programming language independent interface for search algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494–508. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  20. Knowles, J.D., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK-Report No. 214, Computer Engineering and Networks Laboratory, ETH Zürich, Switzerland (2006)

    Google Scholar 

  21. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.D.: Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)

    Article  Google Scholar 

  22. Fonseca, C.M., Fleming, P.J.: On the performance assessment and comparison of multiobjective optimizers. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN IV. LNCS, vol. 1141, pp. 584–593. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Intelligent Systems, Jožef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia

    Tea Tušar & Bogdan Filipič

Authors
  1. Tea Tušar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bogdan Filipič
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Shigeru Obayashi Kalyanmoy Deb Carlo Poloni Tomoyuki Hiroyasu Tadahiko Murata

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Tušar, T., Filipič, B. (2007). Differential Evolution versus Genetic Algorithms in Multiobjective Optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-70928-2_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70927-5

  • Online ISBN: 978-3-540-70928-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation