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LACAIS: Learning Automata Based Cooperative Artificial Immune System for Function Optimization

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Contemporary Computing (IC3 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 94))

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Abstract

Artificial Immune System (AIS) is taken into account from evolutionary algorithms that have been inspired from defensive mechanism of complex natural immune system. For using this algorithm like other evolutionary algorithms, it should be regulated many parameters, which usually they confront researchers with difficulties. Also another weakness of AIS especially in multimodal problems is trapping in local minima. In basic method, mutation rate changes as only and most important factor results in convergence rate changes and falling in local optima. This paper presented two hybrid algorithm using learning automata to improve the performance of AIS. In the first algorithm entitled LA-AIS has been used one learning automata for tuning the hypermutation rate of AIS and also creating a balance between the process of global and local search. In the second algorithm entitled LA-CAIS has been used two learning automata for cooperative antibodies in the evolution process. Experimental results on several standard functions have shown that the two proposed method are superior to some AIS versions.

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References

  1. Wang, Y.J., Zhang, J.S., Zhang, G.Y.: A Dynamic Clustering based Differential Evolution Algorithm for Global Optimization. European Journal of Operational Research 183, 56–73 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  2. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  3. Hedar, A.R., Fulushima, M.: Tabu Search Directed by Direct Search Methods for Nonlinear Global Optimization. European Journal of Operational Research 170, 329–349 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  4. Fogel, D.B., Michalwicz, Z.: Evolutionary Computation 1 - Basic Algorithms and Operators. Institute of Physics (IoP) Publishing, Bristol (2000)

    MATH  Google Scholar 

  5. Herrera, F., Lozano, M., Molina, D.: Continuous Scatter Search: An Analysis of the Integration of Some Combination Methods and Improvement Strategies. European Journal of Operational Research 169(2), 450–476 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  6. Hedar, A., Fukushima, M.: Evolution Strategies Learned with Automatic Termination Criteria. In: SCIS&ISIS 2006, Tokyo, Japan (2006)

    Google Scholar 

  7. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948. IEEE Press, Los Alamitos (1995)

    Google Scholar 

  8. Price, K., Storn, R., Lampinen, J.: Differential Evolution - A Practical Approach to Global Optimization. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  9. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential Evolution Algorithm with Strategy Adaptation for Global Numerical Optimization. IEEE Transactions on Evolutionary Computation 13(2), 398–417 (2009)

    Article  Google Scholar 

  10. Gong, M., Jiao, L., Zhang, X.: A Population-based Artificial Immune System for Numerical Optimization. Neurocomputing 72(1-3), 149–161 (2008)

    Article  Google Scholar 

  11. Bozorgzadeh, M.A., Rahimi, A., Shiry, S.: A Novel Approach for Global Optimization in High Dimensions. In: 12th Annual CSI Computer Conference of Iran, Tehran, Iran, pp. 1–8 (2007)

    Google Scholar 

  12. Vanderplaats, G.N.: Numerical Optimization Techniques for Engineering Design with Applications. McGraw-Hill, New York (1984)

    MATH  Google Scholar 

  13. Huyer, W., Neumaier, A.: SNOBFIT–Stable Noisy Optimization by Branch and Fit. ACM Transactions on Mathematical Software 35(2), 9 (2008)

    Article  MathSciNet  Google Scholar 

  14. Hashemi, A.B., Meybodi, M.R.: A Note on the Learning Automata based Algorithms for Adaptive Parameter Selection in PSO. Journal of Applied Soft Computing (2010) (to appear)

    Google Scholar 

  15. Timmis, J., Hone, A., Stibor, T., Clark, E.: Theoretical advances in artificial immune systems. Theoretical Computer Science 403(1), 11–32 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  16. Dasgupta, D.: Artificial Immune Systems and their Applications. Springer, New York (1998)

    Google Scholar 

  17. Meybodi, M.R., Beigy, H.: A Note on Learning Automata Based Schemes for Adaptation of BP Parameters. Journal of Neurocomputing 48(4), 957–974 (2002)

    Article  MATH  Google Scholar 

  18. Yongshou, D., Yuanyuan, L., Lei, W., Junling, W., Deling, Z.: Adaptive Immune-Genetic Algorithm for Global Optimization to Multivariable Function. Journal of Systems Engineering and Electronics 18(3), 655–660 (2007)

    Article  Google Scholar 

  19. Wang, X., Gao, X.Z., Ovaska, S.J.: Artificial Immune Optimization Methods and Applications - A Survey. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3415–3420. IEEE Press, Los Alamitos (2004)

    Google Scholar 

  20. Campelo, F., Guimaraes, F.G., Igarashi, H.: Overview of Artificial Immune Systems for Multi-objective Optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 937–951. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  21. Sheybani, M., Meybodi, M.R.: PSO-LA: A New Model for Optimization. In: 12th Annual CSI Computer Conference of Iran, Tehran, Iran, pp. 1162–1169 (2007)

    Google Scholar 

  22. Meybodi, M.R., Kharazmi, M.R.: Application of Cellular Learning Automata to Image Processing. J. Aut. 14(56A), 1101–1126 (2004)

    Google Scholar 

  23. Cutello, V., Nicosia, G.: The Clonal Selection Principle for in Silico and in Vivo Computing. In: Recent Developments in Biologically Inspired Computing, pp. 104–146. Idea Group Publishing (2005)

    Google Scholar 

  24. Timmis, J., Edmonds, C., Kelsey, J.: Assessing the Performance of Two Immune Inspired Algorithms and a Hybrid Genetic Algorithm for Function Optimisation. In: IEEE Congress on Evolutionary Computation, Potland, Oregon, USA, vol. 1, pp. 1044–1051. IEEE Press, Los Alamitos (2004)

    Google Scholar 

  25. De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)

    Article  Google Scholar 

  26. Garrett, S.M.: Parameter-free, adaptive clonal selection. In: IEEE Congress on Evolutionary Computation, Potland, Oregon, USA, vol. 1, pp. 1052–1058 (2004)

    Google Scholar 

  27. Cutello, V., Nicosia, G., Pavone, M.: Real Coded Clonal Selection Algorithm for Unconstrained Global Numerical Optimization using a Hybrid Inversely Proportional Hypermutation Operator. In: 21st Annual ACM Symposium on Applied Computing, Dijon, France, pp. 950–954 (2006)

    Google Scholar 

  28. Narendra, K.S., Thathachar, M.A.L.: Learning Automata: An Introduction. Prentice-Hall Inc., Englewood Cliffs (1989)

    Google Scholar 

  29. Khilwani, N., Prakash, A., Shankar, R., Tiwari, M.: Fast clonal algorithm. Engineering Applications of Artificial Intelligence 21(1), 106–128 (2008)

    Article  Google Scholar 

  30. De Castro, L.N., Von Zuben, F.J.: Recent developments in biologically inspired computing. Igi Global (2004)

    Google Scholar 

  31. Sheybani, M., Meybodi, M.R.: CLA-PSO: A New Model for Optimization. In: 15th Conference on Electrical Engineering, Volume on Computer, Telecommunication Research Center, Tehran, Iran (2007)

    Google Scholar 

  32. Abtahi, F., Meybodi, M.R., Ebadzadeh, M.M., Maani, R.: Learning Automata-Based Co-Evolutionary Genetic Algorithms for Function Optimization. In: IEEE 6th International Symposium on Intelligent Systems, Subotica, Serbia, pp. 1–5. IEEE Press, Los Alamitos (2008)

    Google Scholar 

  33. Ebdali, F., Meybodi, M.R.: Adaptation of Ants colony Parameters Using Learning Automata. In: 10th Annual CSI Computer Conference of Iran, pp. 972–980 (2005)

    Google Scholar 

  34. Masoodifar, B., Meybodi, M.R., Hashemi, M.: Cooperative CLA-EC. In: 12th Annual CSI Computer Conference of Iran, pp. 558–559 (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer & IT Engineering, Islamic Azad university, Qazvin branch, Iran

    Alireza Rezvanian

  2. Department of Computer & IT Engineering, Amirkabir University of Technology, Tehran, Iran

    Mohammad Reza Meybodi

Authors
  1. Alireza Rezvanian
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  2. Mohammad Reza Meybodi
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Editor information

Editors and Affiliations

  1. Dept. of Computer Sciences, University of Florida, 32611, Gainesville, FL, USA

    Sanjay Ranka

  2. University of Florida, Gainesville, Fl, USA

    Arunava Banerjee

  3. Department of Computer Science and Engineering, Indian Institute of Technology, 110016, New Delhi, INDIA

    Kanad Kishore Biswas

  4. Computer Science, College of Engineering and Science, Louisiana Tech University, LA 71272, Ruston, USA

    Sumeet Dua

  5. University of Florida, Gainesville, FL, USA

    Prabhat Mishra

  6. Department of Computer Science & Engineering, Indian Institute of Technology, 208016, Kanpur, India

    Rajat Moona

  7. National Tsing Hua University, Hsin-Chu, Taiwan, R.O.C.

    Sheung-Hung Poon

  8. Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong

    Cho-Li Wang

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© 2010 Springer-Verlag Berlin Heidelberg

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Rezvanian, A., Meybodi, M.R. (2010). LACAIS: Learning Automata Based Cooperative Artificial Immune System for Function Optimization. In: Ranka, S., et al. Contemporary Computing. IC3 2010. Communications in Computer and Information Science, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14834-7_7

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  • DOI: https://doi.org/10.1007/978-3-642-14834-7_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14833-0

  • Online ISBN: 978-3-642-14834-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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