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Interactive Cone Contraction for Evolutionary Mutliple Objective Optimization

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Advances in Data Analysis with Computational Intelligence Methods

Part of the book series: Studies in Computational Intelligence ((SCI,volume 738))

Abstract

We present a new interactive evolutionary algorithm for Multiple Objective Optimization (MOO) which combines the NSGA-II method with a cone contraction method. It requires the Decision Maker (DM) to provide preference information in form of a reference point and pairwise comparisons of solutions from a current population. This information is represented with a compatible Achievement Scalarizing Function (ASF) which is used to guide the evolutionary search towards the most preferred region of the Pareto front. The performance of the proposed algorithm is illustrated on a set of benchmark problems. The experimental results confirm its ability to converge quickly to the DM’s most preferred region. Its competitive advantage over the state-of-the-art method, called NEMO-0, is increasing when the DM provides a richer preference information composed of a greater number of pairwise comparisons of solutions.

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References

  1. Abraham, A., Jain, L.C., Goldberg, R.: Evolutionary Multiobjective Optimization: Theoretical Advances and Applications (Advanced Information and Knowledge Processing). Springer, New York (2005)

    Book  MATH  Google Scholar 

  2. Battiti, R., Passerini, A.: Brain-computer evolutionary multiobjective optimization: a genetic algorithm adapting to the decision maker. IEEE Trans. Evol. Comput. 14(5), 671–687 (2010)

    Article  Google Scholar 

  3. Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.): Multiobjective optimization: interactive and evolutionary approaches. LNCS, vol. 5252. Springer, Berlin (2008)

    Google Scholar 

  4. Branke, J., Greco, S., Słowiński, R., Zielniewicz, P.: Learning value functions in interactive evolutionary multiobjective optimization. IEEE Trans. Evolut. Comput. 19(1), 88–102 (2015)

    Article  MATH  Google Scholar 

  5. Branke, J., Corrente, S., Greco, S., Słowiński, R., Zielniewicz, P.: Using Choquet intergral as preference model in interactive evolutionary multiobjective optimization. Eur. J. Oper. Res. 250(3), 884–901 (2016)

    Article  MATH  Google Scholar 

  6. Ciomek, K., Kadziński, M., Tervonen, T.: Heuristics for prioritizing pair-wise elicitation questions with additive multi-attribute value models. Omega 71, 27–45 (2017)

    Google Scholar 

  7. Deb, K., Sinha, A., Korhonen, P., Wallenius, J.: An interactive evolutionary multiobjective optimization method based on progressively approximated value functions. IEEE Trans. Evolut. Comput. 14(5), 723–730 (2010)

    Article  Google Scholar 

  8. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evolut. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  9. Fonseca, C., Fleming, P.: Genetic algorithms for multiobjective optimization: Formulation, discussion, and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423 (1993)

    Google Scholar 

  10. Kadziński, M., Słowiński, R.: Interactive robust cone contraction method for multiple objective optimization problems. Int. J. Inf. Technol. Decis. Making 11(2), 327–357 (2012)

    Article  Google Scholar 

  11. Kadziński, M., Tervonen, T., Tomczyk, M., Dekker, R.: Evaluation of multi-objective optimization approaches for solving green supply chain design problem. Omega 68, 168–184 (2017)

    Article  Google Scholar 

  12. Kadziński, M., Tomczyk, M.: Interactive Evolutionary Multiple Objective Optimization for Group Decision. Group Decision and Negotiation 26(4), 693–728 (2017)

    Google Scholar 

  13. Phelps, S., Köksalan, M.: An interactive evolutionary metaheuristic for multi-objective combinatorial optimization. Manag. Sci. 49(12), 1726–1738 (2003)

    Article  MATH  Google Scholar 

  14. Tervonen, T., van Valkenhoef, G., Basturk, N., Postmus, D.: Hit-And-Run enables efficient weight generation for simulation-based multiple criteria decision analysis. Eur. J. Oper. Res. 224(3), 552–559 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  15. Wierzbicki, A.P.: On the completeness and constructiveness of parametric characterizations to vector optimization problems. OR Spektrum 8, 73–87 (1986)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

Miłosz Kadziński and Michał Tomczyk acknowledge financial support from the Polish National Science Center (grant no. DEC-2013/11/D/ST6/03056).

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Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, 60-965, Poznań, Poland

    Miłosz Kadziński, Michał K. Tomczyk & Roman Słowiński

  2. Systems Research Institute Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

Authors
  1. Miłosz Kadziński
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  2. Michał K. Tomczyk
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  3. Roman Słowiński
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Corresponding author

Correspondence to Roman Słowiński .

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Editors and Affiliations

  1. Department of Medicine, Division of Nephrology and Hypertension, University of Louisville, Louisville, Kentucky, USA

    Adam E Gawęda

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Institute of Computational Intelligence, Department of Mechanical Engineering and Computer Science, Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  4. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, Oklahoma, USA

    Gary G. Yen

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Kadziński, M., Tomczyk, M.K., Słowiński, R. (2018). Interactive Cone Contraction for Evolutionary Mutliple Objective Optimization. In: Gawęda, A., Kacprzyk, J., Rutkowski, L., Yen, G. (eds) Advances in Data Analysis with Computational Intelligence Methods. Studies in Computational Intelligence, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-319-67946-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-67946-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67945-7

  • Online ISBN: 978-3-319-67946-4

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