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DEMO: Differential Evolution for Multiobjective Optimization

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Evolutionary Multi-Criterion Optimization (EMO 2005)

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

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Abstract

Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications. In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective optimization based on DE. DEMO combines the advantages of DE with the mechanisms of Pareto-based ranking and crowding distance sorting, used by state-of-the-art evolutionary algorithms for multiobjective optimization. DEMO is implemented in three variants that achieve competitive results on five ZDT test problems.

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References

  1. 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 

  2. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland (2001)

    Google Scholar 

  3. Price, K.V., Storn, R.: Differential evolution – a simple evolution strategy for fast optimization. Dr. Dobb’s Journal 22, 18–24 (1997)

    Google Scholar 

  4. Lampinen, J.: A bibliography of differential evolution algorithm, http://www2.lut.fi/~jlampine/debiblio.htm

  5. Abbass, H.A., Sarker, R., Newton, C.: PDE: A pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the Congress on Evolutionary Computation 2001 (CEC’2001), Piscataway, New Jersey, vol. 2, pp. 971–978. IEEE Service Center, Los Alamitos (2001)

    Google Scholar 

  6. Abbass, H.A.: The self-adaptive pareto differential evolution algorithm. In: Congress on Evolutionary Computation (CEC’2002), vol. 1, pp. 831–836. IEEE Computer Society Press, Piscataway (2002)

    Google Scholar 

  7. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3, 257–271 (1999)

    Article  Google Scholar 

  8. Madavan, N.K.: Multiobjective optimization using a pareto differential evolution approach. In: Congress on Evolutionary Computation (CEC’2002), vol. 2, pp. 1145–1150. IEEE Service Center, Piscataway (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, pp. 862–869. IEEE Press, Canberra (2003)

    Google Scholar 

  10. Storn, R.: Differential evolution homepage, http://www.icsi.berkeley.edu/~storn/code.html

  11. Thomsen, R.: Multimodal optimization using crowding-based differential evolution. In: 2004 Congress on Evolutionary Computation (CEC 2004), vol. 1, pp. 1382–1389. IEEE Service Center, Portland (2004)

    Google Scholar 

  12. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8, 173–195 (2000)

    Article  Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Robič, T., Filipič, B. (2005). DEMO: Differential Evolution for Multiobjective 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_36

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  • DOI: https://doi.org/10.1007/978-3-540-31880-4_36

  • 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)

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