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Immune Based Chaotic Artificial Bee Colony Multiobjective Optimization Algorithm

  • Xia Zhou
  • Jiong Shen
  • Yiguo Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)

Abstract

This work presents a new multiobjective optimization algorithm based on artificial bee colony, named the ICABCMOA. In order to meet the requirements of Pareto-based approaches, a new fitness assignment function is defined based on the dominated number. In the ICABCMOA, a high-dimension chaotic method based on Tent map is addressed to increase the searching efficiency. Vaccination and gene recombination are adopted to promote the convergence. The experimental results of the ICABCMOA compared with NSGAII and SPEA2 over a set of test functions show that it is an effective method for high-dimension optimization problems.

Keywords

Immune Multiobjective Chaotic Artificial Bee Colony 

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References

  1. 1.
    Karaboga, D.: An Idea Based on Honey bee Swarm for Numerical Optimization. Technical Report, Computer Engineering Department. Erciyes University,Turkey (2005)Google Scholar
  2. 2.
    Zhou, A., Qu, B.Y., Li, H., et al.: Multiobjective Evolutionary Algorithms: a Survey of the State-of-the-art. Journal of Swarm and Evolutionary Computation 1(1), 32–49 (2011)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Zhou, X., Shen, J., Sheng, J.X.: An Immune Recognition Based Algorithm for Finding Non-dominated Set in Multiobjective Optimization. In: IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Wuhan, China, pp. 305–310 (2008)Google Scholar
  4. 4.
    Shan, L., Qiang, H., Li, J., et al.: Chaotic Optimization Algorithm Based on Tent Map. Control and Decision 20(2), 179–182 (2005) (in Chinese)zbMATHGoogle Scholar
  5. 5.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multi-objective Optimization. In: Evolutionary Methods for Design, Optimization and Control, Barcelona, Spain, pp. 19–26 (2002)Google Scholar
  6. 6.
    Deb, K., Pratap, A., Agarwal, S., et al.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  7. 7.
    Zitzler, E.K., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. IEEE Transactions on Evolutionary Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar
  8. 8.
    Schott, J.T.: Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xia Zhou
    • 1
  • Jiong Shen
    • 2
  • Yiguo Li
    • 2
  1. 1.School of Mechanical & Electrical EngineeringJinling Institute of TechnologyNanjingP.R. China
  2. 2.School of Energy & EnvironmentSoutheast UniversityNanjingP.R. China

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