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Effects of Similarity-Based Selection on WBMOIA: A Weight-Based Multiobjective Immune Algorithm

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Advances in Computation and Intelligence (ISICA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5821))

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Abstract

With a comparison to the random selection approach used in the weight-based multiobjective immune algorithm (WBMOIA), this paper proposes a new selection approach based on the truncation algorithm with similar individuals (TASI). Then the effect of the proposed selection approach is examined on the performance of WBMOIA. On one hand, the performance is compared between WBMOIA with the random selection approach and WBMOIA with the proposed selection approach. On the other hand, simulation results on a number of problems are presented to investigate if there exists any value of the reduction rate where WBMOIA performs well. Experiment results show that the performance of WBMOIA can be improved by the proposed selection approach and a better reduction rate can be obtained for each test problem.

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References

  1. de Castro, L.N., Timmis, J.: Artifical Immune System: A New Computational Intelligence Approach. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  2. Hart, E., Timmis, J.: Application Areas of AIS: The Past, the Present and the Future. Appl. Soft. Comput. 3, 191–201 (2008)

    Article  Google Scholar 

  3. Yoo, J., Hajela, P.: Immune Network Simulations in Multicriterion Design. Struct. Opt. 18, 85–94 (1999)

    Article  Google Scholar 

  4. Coello Coello, C.A., Cruz Cortés, N.: An Approach to Solve Multiobjective Optimization Problems Based on an Artificial Immune System. In: Timmis, J., Bentley, P.J. (eds.) First International Conference on Artificial Immune Systems (ICARIS 2002), pp. 212–221. University of Kent, Canterbury (2002)

    Google Scholar 

  5. Coello Coello, C.A., Cruz Cortés, N.: Solving Multiobjective Optimization Problems Using an Artificial Immune System. Genet. Prog. Evol. Mach. 6, 163–190 (2005)

    Article  Google Scholar 

  6. de Castro, L.N., Timmis, J.: An Artificial Immune Network for Multimodal Function Optimization. In: Proc. 2002 Congress on Evolutionary Computation, CEC 2002, Honolulu, vol. 1, pp. 699–704. IEEE Press, Los Alamitos (2002)

    Google Scholar 

  7. Freschi, F., Repetto, M.: VIS: An Artificial Immune Network for Multi-Objective Optimization. Eng. Optimiz. 38(18), 975–996 (2006)

    Article  Google Scholar 

  8. Luh, G.C., Chueh, C.H., Liu, W.W.: Multi-Objective Optimal Design of Truss Structure with Immune Algorithm. Comput. Struct. 82, 829–844 (2004)

    Article  MathSciNet  Google Scholar 

  9. Wang, X.L., Mahfouf, M.: ACSAMO: an Adaptive Multiobjective Optimization Algorithm Using the Clonal Selection Principle. In: Proc. 2nd European Symposium on Nature-inspired Smart Information Systems, Puerto de la Cruz, Tenerife, Spain (2006)

    Google Scholar 

  10. Zhang, Z.H.: Multiobjective Optimization Immune Algorithm in Dynamic Environments and Its Application to Greenhouse Control. Appl. Soft. Comput. 8, 959–971 (2008)

    Article  Google Scholar 

  11. Gao, J., Fang, L.: A Novel Artificial Immune System for Multiobjective Optimization Problems. In: Yu, W., He, H., Zhang, N. (eds.) 6th International Symposium on Neural Networks. LNCS, vol. 5553, pp. 88–97. Springer, Heidelberg (2009)

    Google Scholar 

  12. Gao, J., Wang, J.: WBMOAIS: A Novel Artificial Immune System for Multiobjective Optimization. Computers and Operations Research (2009), doi:10.1016/j.cor, 03.009

    Google Scholar 

  13. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Ltd., New York (2001)

    MATH  Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Gao, J., Fang, Z., Fang, L. (2009). Effects of Similarity-Based Selection on WBMOIA: A Weight-Based Multiobjective Immune Algorithm. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_21

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  • DOI: https://doi.org/10.1007/978-3-642-04843-2_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04842-5

  • Online ISBN: 978-3-642-04843-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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