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Clustering-Based Selection for Evolutionary Many-Objective Optimization

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Parallel Problem Solving from Nature – PPSN XIII (PPSN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8672))

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Abstract

This paper discusses a selection scheme allowing to employ a clustering technique to guide the search in evolutionary many-objective optimization. The underlying idea to avoid the curse of dimensionality is based on transforming the objective vectors before applying a clustering and the selection of cluster representatives according to the distance to a reference point. The experimental results reveal that the proposed approach is able to effectively guide the search in high-dimensional objective spaces, producing highly competitive performance when compared with state-of-the-art algorithms.

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References

  1. Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: A short review. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2008 (2008)

    Google Scholar 

  2. Denysiuk, R., Costa, L., Espírito Santo, I.: Many-objective optimization using differential evolution with variable-wise mutation restriction. In: Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2013, pp. 591–598 (2013)

    Google Scholar 

  3. Deb, K., Pratap, A., Agarwal, 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., 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.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Li, H., Zhang, Q.: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation 13(2), 284–302 (2009)

    Article  Google Scholar 

  6. Hughes, E.J.: Multiple single objective Pareto sampling. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2003, pp. 2678–2684 (2003)

    Google Scholar 

  7. Hughes, E.J.: MSOPS-II: A general-purpose many-objective optimiser. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2007, pp. 3944–3951 (2007)

    Google Scholar 

  8. Bader, J., Zitzler, E.: HypE: An algorithm for fast hypervolume-based many-objective optimization. Evolutionary Computation 19(1), 45–76 (2011)

    Article  Google Scholar 

  9. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. Technical Report 112, Swiss Federal Institute of Technology, Zurich, Switzerland (2001)

    Google Scholar 

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

    Article  Google Scholar 

  11. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - A comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–304. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  12. Bosman, P.A.N., Thierens, D.: The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7(2), 174–188 (2003)

    Article  Google Scholar 

  13. García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: A case study on the CEC 2005 special session on real parameter optimization. Journal of Heuristics 15(6), 617–644 (2009)

    Article  MATH  Google Scholar 

  14. 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 

  15. Durillo, J.J., Nebro, A.J.: jMetal: A Java framework for multi-objective optimization. Advances in Engineering Software 42(10), 760–771 (2011)

    Article  Google Scholar 

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Denysiuk, R., Costa, L., Espírito Santo, I. (2014). Clustering-Based Selection for Evolutionary Many-Objective Optimization. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_53

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  • DOI: https://doi.org/10.1007/978-3-319-10762-2_53

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10761-5

  • Online ISBN: 978-3-319-10762-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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