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Coevolutionary Multi-objective Optimization Using Clustering Techniques

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MICAI 2005: Advances in Artificial Intelligence (MICAI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3789))

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Abstract

We propose a new version of a multiobjective coevolutionary algorithm. The main idea of the proposed approach is to concentrate the search effort on promising regions that arise during the evolutionary process as a product of a clustering mechanism applied on the set of decision variables corresponding to the known Pareto front. The proposed approach is validated using several test functions taken from the specialized literature and it is compared with respect to its previous version and another approach that is representative of the state-of-the-art in evolutionary multiobjective optimization.

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References

  1. Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York (2002)

    MATH  Google Scholar 

  2. 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 

  3. Knowles, J.D., Corne, D.W.: Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation 8, 149–172 (2000)

    Article  Google Scholar 

  4. Coello Coello, C.A., Reyes Sierra, M.: A Coevolutionary Multi-Objective Evolutionary Algorithm. In: Proceedings of 2003 CEC, vol. 1, pp. 482–489. IEEE Press, Los Alamitos (2003)

    Google Scholar 

  5. Paredis, J.: Coevolutionary algorithms. In: Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.) The Handbook of Evolutionary Computation, 1st supplement, pp. 225–238. Institute of Physics Publishing and Oxford University Press, Oxford (1998)

    Google Scholar 

  6. Potter, M., Jong., K.D.: A cooperative coevolutionary approach to function optimization. In: Proceedings from PPSN V, pp. 530–539. Springer, Heidelberg (1994)

    Google Scholar 

  7. Parmee, I.C., Watson, A.H.: Preliminary Airframe Design Using Co-Evolutionary Multiobjective Genetic Algorithms. In: Proceedings of GECCO 1999, vol. 2, pp. 1657–1665. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  8. Keerativuttitumrong, N., Chaiyaratana, N., Varavithya, V.: Multi-objective Co-operative Co-evolutionary Genetic Algorithm. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 288–297. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  9. Tan, K., Chew, Y., Lee, T., Yang, Y.: A cooperative coevolutionary algorithm for multiobjective optimization. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 390–395. IEEE Press, Los Alamitos (2003)

    Google Scholar 

  10. Iorio, A., Li, X.: A cooperative coevolutionary multiobjective algorithm using non-dominated sorting. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 537–548. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  11. Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423. Morgan Kauffman Publishers, San Francisco (1993)

    Google Scholar 

  12. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, New Jersey (1988)

    MATH  Google Scholar 

  13. Van Veldhuizen, D.A.: Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Air Force Institute of Technology, Wright-Patterson AFB, Ohio (1999)

    Google Scholar 

  14. Van Veldhuizen, D.A., Lamont, G.B.: On Measuring Multiobjective Evolutionary Algorithm Performance. In: 2000 CEC, vol. 1, pp. 204–211. IEEE Service Center, Los Alamitos (2000)

    Google Scholar 

  15. Schott, J.R.: Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts (1995)

    Google Scholar 

  16. 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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Sierra, M.R., Coello, C.A.C. (2005). Coevolutionary Multi-objective Optimization Using Clustering Techniques. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_61

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  • DOI: https://doi.org/10.1007/11579427_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29896-0

  • Online ISBN: 978-3-540-31653-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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