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A Generalized Differential Evolution Combined with EDA for Multi-objective Optimization Problems

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Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence (ICIC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5227))

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Abstract

This paper proposed a multi-objective evolutionary algorithm (called by GDE-EDA hereinafter). The proposed algorithm combined a generalized differential evolution (DE) with an estimation of distribution algorithm (EDA). This combination can simultaneously use global information of population extracted by EDA and differential information by DE. Thus, GDE-EDA can obtain a better distribution of the solutions by EDA while keeping the fast convergence exhibited by DE. The experimental results of the proposed GDE-EDA algorithm were reported on a suit of widely used test functions, and compared with GDE and NSGA-II in the literature.

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References

  1. Storn, R., Price, K.: Differential Evolution—A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization 11(4), 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  2. Chang, C.S., Xu, D.Y., Quek, H.B.: Pareto-Optimal Set Based Multiobjective Tuning of Fuzzy Automatictrain Operation for Mass Transit System. Electric Power Applications, IEE Proceedings 146(5), 577–583 (1999)

    Article  Google Scholar 

  3. Bergey, P.K.: An Agent Enhanced Intelligent Spreadsheet Solver for Multi-Criteria Decision Making. In: Proceddings of the Fifth Americas Conference on Information Systems, Milwaukee, Wisconsin, USA, pp. 966–968 (1999)

    Google Scholar 

  4. Abbass, H.A.: The Self-Adaptive Pareto Differential Evolution Algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002 (2002)

    Google Scholar 

  5. Iorio, A.W., Li, X.: Solving Rotated Multi-Objective Optimization Problems Using Differential Evolution. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 861–872. Springer, Heidelberg (2004)

    Google Scholar 

  6. Deb, K., et al.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  MathSciNet  Google Scholar 

  7. Iorio, A.W., Li, X.: Incorporating Directional Information within a Differential Evolution Algorithm for Multi-Objective Optimization. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation (2006)

    Google Scholar 

  8. Santana-Quintero, L.V., Coello, C.A.C.: An Algorithm Based on Differential Evolution for Multi-Objective Problems. International Journal of Computational Intelligence Research 1(2), 151–169 (2005)

    Article  MathSciNet  Google Scholar 

  9. Kukkonen, S., Lampinen, J.: GDE3: The Third Evolution Step of Generalized Differential Evolution. In: 2005 IEEE Congress on Evolutionary Computation (2005)

    Google Scholar 

  10. Mühlenbein, H., Paaβ, G.: From Recombination of Genes to the Estimation of Distribution Algorithms I. LNCS, vol. 1411, pp. 178–187 (1996)

    Google Scholar 

  11. Sun, J., Zhang, Q., Tsang, E.P.K.: DE/EDA: A New Evolutionary Algorithm for Global Optimization. Information Sciences 169(3-4), 249–262 (2005)

    Article  MathSciNet  Google Scholar 

  12. Zhou, A., Zhang, Q., Jin, Y., et al.: A Model-based Evolutionary Algorithm for Bi-Objective Optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2568–2575 (2005)

    Google Scholar 

  13. Bosman, P.A.N., Thierens, D.: Multi-Objective Optimization with Diversity Preserving Mixture-Based Iterated Density Estimation Evolutionary Algorithms. International Journal of Approximate Reasoning 31(3), 259–289 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  14. Larraňaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2002)

    MATH  Google Scholar 

  15. Nebro, A.J., et al.: A Cellular Genetic Algorithm for Multiobjective Optimization. NICSO, pp. 25–36 (2006)

    Google Scholar 

  16. Deb, K., et al.: Scalable Test Problems for Evolutionary Multi-Objective Optimization. Evolutionary Multiobjective Optimization, pp. 105–145 (2005)

    Google Scholar 

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Authors and Affiliations

  1. School of Mechanical Engineering, Dalian University of Technology, Dalian, P.R. China, 116024

    Wang Chen, Yan-jun Shi & Hong-fei Teng

Authors
  1. Wang Chen
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  2. Yan-jun Shi
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  3. Hong-fei Teng
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Editor information

De-Shuang Huang Donald C. Wunsch II Daniel S. Levine Kang-Hyun Jo

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© 2008 Springer-Verlag Berlin Heidelberg

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Chen, W., Shi, Yj., Teng, Hf. (2008). A Generalized Differential Evolution Combined with EDA for Multi-objective Optimization Problems. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_18

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  • DOI: https://doi.org/10.1007/978-3-540-85984-0_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85983-3

  • Online ISBN: 978-3-540-85984-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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