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A Novel Hybrid Evolutionary Algorithm for Solving Multi-Objective Optimization Problems

  • Huantong Geng
  • Haifeng Zhu
  • Rui Xing
  • Tingting Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

This paper proposed a novel hybrid evolutionary algorithm for solving the multi-objective optimization problems (MOPs). The algorithm uses the idea of simulated annealing to combine co-evolution with genetic evolution, set several model sets according to the principle of “model student”, and employs ε-dominant and crowding distance sorting to search the excellent population. In the co-evolution model, cluster analysis is used to classify the model set, and the estimation of distribution algorithm (EDA) is used to establish the probabilistic model for each class. The individuals are generated by sampling through the probabilistic model; in genetic evolution model, populations evolve based on the model set. This algorithm takes full advantage of the global and local search abilities, and makes comparison with the classical algorithm NSGA-II, the experimental results show that our algorithm for solving the multi-objective problem has better convergence and distribution.

Keywords

MOEAs Co-evolution EDA Model Student 

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References

  1. 1.
    Horn, J., Nafpliotis, N., Goldberg, D.E.: A Niched Pareto Genetic Algorithm for Multi-objective Optimization. In: Michalewicz, Z. (ed.) Proc. lst IEEE Conf. on Evolutionary Computation, pp. 82–87. IEEE Service Center, Piscataway (1994)Google Scholar
  2. 2.
    Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)zbMATHGoogle Scholar
  3. 3.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Zurich, Switzerland, ETH Zurich, pp. 1–21 (2001)Google Scholar
  4. 4.
    Deb, K., Pratap, A., Agarwal, S., et al.: A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  5. 5.
    Laumanns, M., Thiele, L., Deb, K., et al.: Combining Convergence and Diversity in Evolutionary Multi-objective Optimization. Evolutionary Computation 10(3), 263–282 (2002)CrossRefGoogle Scholar
  6. 6.
    Geng, H., Zhang, M., Huang, L., Wang, X.: Infeasible Elitists and Stochastic Ranking Selection in Constrained Evolutionary Multi-objective Optimization. In: Wang, T.-D., Li, X., Chen, S.-H., Wang, X., Abbass, H.A., Iba, H., Chen, G.-L., Yao, X. (eds.) SEAL 2006. LNCS, vol. 4247, pp. 336–344. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Geng, H.T., Song, Q.X., Wu, T.T., Liu, J.F.: A Multi-objective Constrained Optimization Algorithm Based on Infeasible Individual Stochastic Binary-Modification. In: Proceedings of 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, pp. 89–93. IEEE, Shanghai (2009)CrossRefGoogle Scholar
  8. 8.
    Potter, M.A., De Jong, K.A.: A Cooperative Coevolutionary Approach to Function Optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  9. 9.
    Zhou, S.D., Sun, Z.Q.: Estimation of Distribution Algorithms. AAS 33(2), 113–124 (2007) (in Chinese)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Duda, O., Hart, E., Stork, G.: Pattern Classification, 2nd edn., pp. 450–452. John Wiley & Sons, Inc., USA (2001)zbMATHGoogle Scholar
  11. 11.
    Deb, K., Thiele, L., Laumanns, M., et al.: Scalable Multi-objective Optimization Test Problems. In: Proceedings of the Congress on Evolutionary Computation, vol. 1, pp. 825–830. IEEE Service Center, Piscataway (2002)Google Scholar
  12. 12.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar
  13. 13.
    Coello, C.A.C.: A Comprehensive Survey of Evolutionary-based Multi-objective Optimization Techniques. Knowledge and Information Systems 1(3), 269–308 (1999)Google Scholar
  14. 14.
    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, MA (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Huantong Geng
    • 1
  • Haifeng Zhu
    • 1
  • Rui Xing
    • 2
  • Tingting Wu
    • 1
  1. 1.College of Computer and SoftwareNanjing University of Information Science & TechnologyNanjingChina
  2. 2.College of Atmospheric ScienceNanjing University of Information Science & TechnologyNanjingChina

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