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A variant of the SOM algorithm and its interpretation in the viewpoint of social influence and learning

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Abstract

The conventional self-organizing feature map (SOM) algorithm is usually interpreted as a computational model, which can capture main features of computational maps in the brain. In this paper, we present a variant of the SOM algorithm called the SOM-based optimization (SOMO) algorithm. The development of the SOMO algorithm was motivated by exploring the possibility of applying the SOM algorithm in continuous optimization problems. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited by the SOMO algorithm. In our opinion, the SOMO algorithm not only can be regarded as a biologically inspired computational model but also may be regarded as a new approach to a model of social influence and social learning. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm.

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References

  1. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  2. DE Goldberg (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  3. Fogel LJ (1994) Evolutionary programming in perspective: the top-down view. In: Zurada JM, Marks RJ II, Robinson CJ (eds) Computational intelligence imitating life. IEEE Press, Piscataway

  4. Rechenberg I (1994) Evolution strategy, in computational intelligence: imitating life. In: Zurada JM, Marks RJ II, Robinson CJ (eds) Computational intelligence imitating life. IEEE Press, Piscataway

  5. Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence. Academic Press, New York

    Google Scholar 

  6. Eberhart R, Kennedy JA (1995) New optimizer using particle swarm theory. In: Proceedings of the 6th international symposium on micro machine and human science, Oct 1995, pp 39–43

  7. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, Dec 1995, vol 4, pp 1942–1948

  8. Kohonen T (1989) Self-organization and associative memory, 3rd edn. Springer, New York

    Google Scholar 

  9. Kohonen T (2001) Self-organization maps, 3rd extended edn. Springer, Heidelberg

  10. Kohonen T, Oja E, Simula O, Visa A, Kangas J (1996) Engineering application of the self-organizing map. Proc IEEE 84(10):1358–1383

    Google Scholar 

  11. Willshaw DJ, von der Malsburg C (1976) How patterned neural connections can be set up by self-organization. Proc R Soc Lond Ser B 194:431–445

    Google Scholar 

  12. Kaski S, Kangas J, Kohonen T (1998) Bibliography of self-organizing map (SOM) papers: 1981–1997. Neural Comput Surv 1:102–350

    Google Scholar 

  13. Oja M, Kaski S, Kohonen T (2003) Bibliography of self-organizing map (SOM) papers: 1998–2001 addendum. Neural Comput Surv 3(1):1–156

    Google Scholar 

  14. Yin H (2008) The self-organizing maps: background, theories, extensions and applications. Springer, Berlin

  15. Angeniol B, Vaubois GDLC, Texier JYL (1988) Self-origanizing feature maps and the travelling salesman problem. Neural Netw 1:289–293. doi:10.1016/0893-6080(88)90002-0

    Google Scholar 

  16. Fujimura K, Tokutaka H, Ohshima Y, Kishida S (1994) The traveling salesman problem applied to the self-organizing feature map. In: Proceedings of international conference on neural information processing, pp 427–432

  17. Fujimura K, Tokutaka H (1999) SOM-TSP: an approach to optimize surface component mounting on a printed circuit board. In: Kohonen maps. Elsevier, Amsterdam, pp 219–230

  18. Fujimura K, Kishida S, Kwaw OC, Tokutaka H (2001) Optimization of electronic chip-mounting machine using SOM-TSP method with 5 dimensional data. In: Proceedings of international conference on info-tech and info-net (ICII 2001-Beijing), vol 4, pp 26–31

  19. Vieira F, Neto A, Cosya J (2002) An efficient approach of the SOM algorithm to the traveling salesman problem. In: Proceedings of the neuro networks (SBRN 2002), p 152

  20. Dong JH, Sak LK, Leung WM, Yu ZB (2003) An efficient self-organizing map designed by genetic algorithms for the traveling salesman problem. IEEE Trans Syst Man Cybern 33:877–888. doi:10.1109/TSMCB.2002.804367

    Article  Google Scholar 

  21. Su MC, Zhao YX, Lee J (2004) SOM-based optimization. In: IEEE international joint conference on neural networks, Budapest, pp 781–786

  22. Su MC, Chang HC (2000) Fast self-organizing feature map algorithm. IEEE Trans Neural Netw 13(3):721–733

    Google Scholar 

  23. Su MC, Liu TK, Chang HT (1999) An efficient initialization scheme for the self-organizing feature map algorithm. IEEE international joint conference on neural networks, Washington, DC, pp 1906–1910

  24. De Jong KA (1975) An analysis of the behaviour of a class of genetic adaptive systems. University of Michigan, Ann Arbor, University Microfilms No. 76-9381

    Google Scholar 

  25. Fogel GB, Greenwood GW, Chellapilla K (2000) Evolutionary computation with extinction: experiments and analysis. In: Proceedings of the 2000 congress on evolutionary computation, pp 1415–1420

  26. Salomon R (1995) Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions—a survey of some theoretical and practical aspects of genetic algorithms. BioSystems 39:263–278

    Article  Google Scholar 

  27. The software packages for the GAs and the PSO algorithm. www.engr.iupui.edu/~ebergart/web/PSObook.html

  28. Blake CL, Merz CJ (1998) UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html

  29. Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm optimization. In: IEEE international conference on evolutionary computation, Dec 1999, pp 1931–1938

  30. Kennedy J, Mendes R (2002) Topological structure and particle swarm performance. In: Proceedings of the congress on evolutionary computation, May 2002

  31. Mendes R, Kennedy J, Neves J (2002) Watch thy neighbor or how the swarm can learn from its environment. In: Iberoamerican conference on artificial intelligence, Seville, Nov 2002

  32. Kennedy J, Mendes R (2003) Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms. In: IEEE SMC workshop on soft computing in industrial applications, Jun 2003

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Acknowledgments

This work was partly supported by the National Science Council, Taiwan, ROC, under NSC 97-2631-S-008-003 and NSC 97-2631-H-008-001.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Central University, Chungli, Taiwan

    Mu-Chun Su

  2. Department of Computer Science and Information Engineering, Ta Hwa Institute of Technology, Hsinchu, Taiwan

    Yu-Xiang Zhao

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  1. Mu-Chun Su
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  2. Yu-Xiang Zhao
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Correspondence to Mu-Chun Su.

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Su, MC., Zhao, YX. A variant of the SOM algorithm and its interpretation in the viewpoint of social influence and learning. Neural Comput & Applic 18, 1043–1055 (2009). https://doi.org/10.1007/s00521-009-0278-7

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