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A New Method of Cooperative PSO: Multiple Particle Swarm Optimizers with Inertia Weight with Diversive Curiosity

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 110)

Abstract

This chapter presents a new method of cooperative PSO—multiple particle swarm optimizers with inertia weight with diversive curiosity (MPSOIWα /DC). Compared to a plain MPSOIW, it has the following outstanding features: (1) Decentralization in multi-swarm exploration with hybrid search, (2) Concentration in evaluation and behavior control with diversive curiosity (DC), (3) Practical use of the results of an evolutionary PSOIW, and (4) Their effective combination. The actualization of its overall composition expands the applied object of cooperative PSO, and effectually alleviates stagnation in optimization with the multi-swarm’s decision-making. To demonstrate the effectiveness of the proposal, computer experiments on a suite of multi-dimensional benchmark problems are carried out. We examine its intrinsic characteristics, and compare the search performance with other methods. The obtained experimental results clearly indicate that the search performance of the MPSOIWα/DC is superior to that of the PSOIW, OPSO, and RGA/E, and is better than that of the MPSOα/DC, and MCPSOα/DC except for the Rosenbrock Problem.

Keywords

Cooperative particle swarm optimization Hybrid search Localized random search Diversive and specific curiosity Swarm intelligence 

Notes

Acknowledgment

This research was supported by Grant-in-Aid Scientific Research(C) (22500132) from the Ministry of Education, Culture, Sports, Science and Technology, Japan.

References

  1. 1.
    Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evolut Comput 8(3):225–239.CrossRefGoogle Scholar
  2. 2.
    Van den Bergh F (2002) An analysis of particle swarm optimizers, Ph.D thesis, Department of Computer Science, University of Pretoria, Pretoria, South Africa.Google Scholar
  3. 3.
    Berlyne D (1960) Conflict, arousal, and curiosity. McGraw-Hill Book Co, New York.CrossRefGoogle Scholar
  4. 4.
    Chang JF, Chu SC, Roddick JF, Pan JS (2005) A parallel particle swarm optimization algorithm with communication strategies. J Inf Sci Eng 21:809–818.Google Scholar
  5. 5.
    Clerc M, Kennedy J (2000) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evolut Comput 6(1):58–73.CrossRefGoogle Scholar
  6. 6.
    Clerc M (2006) Particle swarm optimization. ISTE Ltd., London.CrossRefMATHGoogle Scholar
  7. 7.
    Day H (1982) Curiosity and the interested explorer. Perform Instr 21(4):19–22.CrossRefGoogle Scholar
  8. 8.
    Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan, 4–6 October, pp 39–43.Google Scholar
  9. 9.
    Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particleswarm optimization. In: Proceedings of IEEE congress on evolutionary computation (CEC2000), San Diego, CA, USA, 16–19 July, vol 1, pp 84–88.Google Scholar
  10. 10.
    El-Abd E, Kamel MS (2008) A taxonomy of cooperative particle swarm optimizers. Int J Comput Intell Res 4(2):137–144.Google Scholar
  11. 11.
    Fogel LJ (1999) Intelligence through simulated evolution: forty years of evolutionary programming. John Wiley & Sons, Inc. New York.Google Scholar
  12. 12.
    Goldberg DE (1989) Genetic algorithm in search optimization and machine learning, reading. Addison-Wesley, MA.Google Scholar
  13. 13.
    Hema CR, Paulraj MP, Nagarajan R, Yaacob S, Adom AH (2008) Application of particle swarm optimization for EEG signal classification. Biomed Soft Comput Human Sci 13(1):79–84.Google Scholar
  14. 14.
    Juang C-F (2004) A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cyb B 34(2):997–1006.CrossRefGoogle Scholar
  15. 15.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, Perth, Australia, 27 November–1 December, pp 1942–1948.Google Scholar
  16. 16.
    Loewenstein G (1994) The psychology of curiosity: a review and reinterpretation. Psychol Bull 116(1):75–98.CrossRefGoogle Scholar
  17. 17.
    Meissner M, Schmuker M, Schneider G (2006) Optimized particle swarm optimization (OPSO) and its application to artificial neural network training. BMC Bioinformatics, 7(125), p 11. http://www.boimedcentral.com/content/pdf/147-2105-7-125.pdf.
  18. 18.
    Moscato P (1989) On evolution, search optimization, genetic algorithms and martial arts: towards memetic algorithms. Technical report caltech concurrent computation program, Report 826, California Institute of Technology, Pasadena, CA 91125.Google Scholar
  19. 19.
    Niu B, Zhu Y, He X (2005) Multi-population Cooperation Particle Swarm Optimization. In: Proceedings of VIII european conference on artificial life (ECAL2005), Canterbury, UK, 5–9 September. LNCS 3630, pp 874–883.Google Scholar
  20. 20.
    Opdal PM (2001) Curiosity, wonder and education seen as perspective development. Studies in philosophy and education, 20(4). Springer, Netherlands, pp 331–344.Google Scholar
  21. 21.
    Poli P, Kennedy J, Blackwell T (2007) Particle swarm optimization—an overview. Swarm Intell 1:33–57.CrossRefGoogle Scholar
  22. 22.
    Shi Y, Eberhart RC (1998) A modified particle swarm optimiser, In: Proceedings of IEEE international conference on evolutionary computation, Anchorage, AK, USA, 4–9 May, pp 69–73.Google Scholar
  23. 23.
    Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y-P, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005. http//:www.ntu.edu.sg/home/epnsugan/index\underline{}files/ CEC-05/Tech-Report-May-30-05.pdfGoogle Scholar
  24. 24.
    Valle YD, Venayagamoorthy GK, Mohagheghi S, Hernandez J-C, Harley RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evolut Comput 12(2):171–195.CrossRefGoogle Scholar
  25. 25.
    Zhang H, Ishikawa M (2008) Improving the performance of particle swarm optimization with diversive curiosity. In: Proceedings of international multiconference of engineers and computer scientists 2008 (IMECS 2008), Hong Kong, 19–21 March, pp 1–6.Google Scholar
  26. 26.
    Zhang H, Ishikawa M (2008) Particle swarm optimization with diversive curiosity—an endeavor to enhance swarm intelligence. IAENG Int J Comput Sci 35(3):275–284.Google Scholar
  27. 27.
    Zhang H, Ishikawa M (2009) Characterization of particle swarm optimization with diversive curiosity. J Neural Comput Appl 18(5):409–415.CrossRefGoogle Scholar
  28. 28.
    Zhang H, Ishikawa M (2010) The performance verification of an evolutionary canonical particle swarm optimizers. Neural Networks 23(4):510–516.CrossRefGoogle Scholar
  29. 29.
    Zhang H (2010) Multiple particle swarm optimizers with diversive curiosity. In: Proceedings of international multiconference of engineers and computersScientists 2010 (IMECS 2010), Hong Kong, 17–19 March. Lecture notes in engineering and computer Science, pp 174–179Google Scholar
  30. 30.
    Zhang H (2010) A new expansion of cooperative particle swarm optimization. In: Proceedings of 17th international conference on neural information processing (ICONIP2010), Sydney, Australia, 22–25 November 2010. Neural information processing—theory and algorithms, Part I (LNCS 6443), pp 593–600.Google Scholar
  31. 31.
    Zhang H (2011) Multiple particle swarm optimizers with inertia weight with diversive curiosity. In: Proceedings of international multiconference of engineers and computer scientists 2011 (IMECS 2011), Hong Kong, 16–18 March. Lecture notes in engineering and computer science, pp 21–26.Google Scholar
  32. 32.
    Zhang H (2011) Assessment of an evolutionary particle swarm optimizer with inertia weight. In: Proceedings of IEEE congress on evolutionary computation (CEC2011), (ISBN 978-1-4244-7834-7), pp 1746–1753, June 5–8, 2011, New Orleans, LA, USA.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Brain Science and EngineeringKyushu Institute of TechnologyKitakyushuJapan

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