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LADPSO: using fuzzy logic to conduct PSO algorithm

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Abstract

Optimization plays a critical role in human modern life. Nowadays, optimization is used in many aspects of human modern life including engineering, medicine, agriculture and economy. Due to the growing number of optimization problems and their growing complexity, we need to improve and develop theoretical and practical optimization methods. Stochastic population based optimization algorithms like genetic algorithms and particle swarm optimization are good candidates for solving complex problems efficiently. Particle swarm optimization (PSO) is an optimization algorithm that has received much attention in recent years. PSO is a simple and computationally inexpensive algorithm inspired by the social behavior of bird flocks and fish schools. However, PSO suffers from premature convergence, especially in high dimensional multi-modal functions. In this paper, a new method for improving PSO has been introduced. The Proposed method which has been named Light Adaptive Particle Swarm Optimization is a novel method that uses a fuzzy control system to conduct the standard algorithm. The suggested method uses two adjunct operators along with the fuzzy system in order to improve the base algorithm on global optimization problems. Our approach is validated using a number of common complex uni-modal/multi-modal benchmark functions and results have been compared with the results of Standard PSO (SPSO2011) and some other methods. The simulation results demonstrate that results of the proposed approach is promising for improving the standard PSO algorithm on global optimization problems and also improving performance of the algorithm.

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References

  1. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, Piscataway, NJ, pp 1942–1948

    Chapter  Google Scholar 

  2. Engelbrecht AP (2007) Computational intelligence, 2nd edn. Wiley, New York

    Book  Google Scholar 

  3. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  4. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput

  5. Pant M, Radha T, Singh VP (2007) Particle swarm optimization: experimenting the distributions of random numbers. In: 3rd Indian int conf on artificial intelligence (IICAI 2007), pp 412–420

    Google Scholar 

  6. Pant M, Thangaraj R, Abraham A (2008) Improved particle swarm optimization with low-discrepancy sequences. In: IEEE cong on evolutionary computation (CEC 2008), Hong Kong

    Google Scholar 

  7. Norouzzadeh MS, Ahmadzadeh MR, Palhang M (2010) Plowing PSO: anovel approach to effectively initializing particle swarm optimization. In: Proceeding of 3rd IEEE international conference on computer science and information technology, Chengdu, China, vol 1, pp 705–709

    Chapter  Google Scholar 

  8. Mendes R, Kennedy J, Neves J (2005) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput, 1(1)

  9. Suganthan PN (1999) Particle swarm optimiser with neighborhood operator. In: Proceedings of the IEEE congress on evolutionary computation, pp 1958–1962

    Google Scholar 

  10. Kennedy J, Mendes R (2002) Population structure and particle performance. In: Proceedings of the IEEE congress on evolutionary computation, pp 1671–1676

    Google Scholar 

  11. Kennedy J (2003) Bare bones particle swarms. In: Proceedings of the IEEE swarm intelligence symposium, pp 80–87

    Google Scholar 

  12. Standard PSO 2007 and 2011, http://particleswarm.info

  13. Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp 101–106

    Google Scholar 

  14. Venter G, Sobieszczanski-Sobieski J (2003) Particle swarm optimization. J Am Inst Aeronaut Astronaut 41(8):1583–1589

    Google Scholar 

  15. Clerc M (2001) Think locally, act locally: the way of life of cheap-PSO, an adaptive PSO. http://clerc.maurice.free.fr/pso/. Technical report

  16. Zheng Y, Ma L, Zhang L, Qian J (2003) On the convergence analysis and parameter selection in particle swarm optimization. In: Proceedings of the international conference on machine learning and cybernetics, pp 1802–1807

    Google Scholar 

  17. Chen M-R, Lu Y-Z, Luo Q (2010) A novel hybrid algorithm with marriage of particle swarm optimization and extremal optimization. Appl Soft Comput J 10(2):367–373

    Article  Google Scholar 

  18. Lim A, Lin J, Xiao F (2007) Particle swarm optimization and hill climbing for the bandwidth minimization problem. Int J Appl Intell 26:175–182

    Article  MATH  Google Scholar 

  19. Shuang B, Chen J, Li Z (2011) Study on hybrid PS-ACO algorithm. Int J Appl Intell 34:64–73

    Article  Google Scholar 

  20. Angeline PJ (1998) Using selection to improve particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp 84–89

    Google Scholar 

  21. Pant M, Thangaraj R, Abraham A (2007) A new PSO algorithm with crossover operator for global optimization problems. In: 2nd international workshop on hybrid artificial intelligence systems

    Google Scholar 

  22. Higashi H, Iba H (2003) Particle swarm optimization with gaussian mutation. In: Proceedings of the IEEE swarm intelligence symposium, pp 72–79

    Google Scholar 

  23. Pant M, Radha T, Singh VP A new diversity based particle swarm optimization using Gaussian mutation. Int J Math Model, Simul Appl

  24. Li C, Yang S, Korejo I An adaptive mutation operator for particle swarm optimization

  25. Li C, Liu Y, Zhou A, Kang L, Wang H A fast particle swarm optimization algorithm with Cauchy mutation and natural selection strategy

  26. van den Bergh F, Engelbrecht AP (2000) Cooperative learning in neural networks using particle swarm optimizers. S Afr Comput J 26:84–90

    Google Scholar 

  27. Silva A, Neves A, Costa E (2002) An empirical comparison of particle swarm and predator prey optimisation. In: Proceedings of the thirteenth Irish conference on artificial intelligence and cognitive science, pp 103–110

    Google Scholar 

  28. Xie X, Zhang W, Yang Z (2002) Adaptive particle swarm optimization on individual level. In: Proceedings of the sixth international conference on signal processing, pp 1215–1218

    Google Scholar 

  29. Xie X, Zhang W, Yang Z (2002) A dissipative particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp 1456–1461

    Google Scholar 

  30. van den Bergh F (2002) An analysis of particle swarm optimizers. In PhD thesis, Department of Computer Science, University of Pretoria, Pretoria, South Africa

  31. Blackwell TM, Bentley PJ (2002) Dynamic search with charged swarms. In: Proceedings of the genetic and evolutionary computation conference, pp 19–26

    Google Scholar 

  32. 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 special session on real-parameter optimization. Nanyang Technological University, Singapore and Kanpur Genetic Algorithms Laboratory, IIT Kanpur, Technical Report

  33. Auger A, Hansen N (2005) A restart CMA evolution strategy with increasing population size. In: IEEE congress on evolutionary computation (CEC2005), vol 2, pp 1785–1791

    Chapter  Google Scholar 

  34. Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: IEEE congress on evolutionary computation (CEC2005), vol 2, pp 1785–1791

    Chapter  Google Scholar 

  35. Qin AK, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer with local search. In: IEEE congress on evolutionary computation (CEC2005), vol 1, pp 522–528

    Chapter  Google Scholar 

  36. Gockenbach MS, Kearsley AJ, Symes WW (1997) An infeasible point method for minimizing the Lennard-Jones potential. Comput Optim Appl 8(3):273–286

    Article  MathSciNet  MATH  Google Scholar 

  37. Fan E (2002) Global optimization of Lennard-Jones atomic clusters. MSc thesis, McMaster University

  38. Gibbons JD (1985) Nonparametric statistical inference. Marcel Dekker, New York

    MATH  Google Scholar 

  39. Hollander M, Wolfe DA (1999) Nonparametric statistical methods. Wiley, Hoboken

    MATH  Google Scholar 

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Authors and Affiliations

  1. Electrical & Computer Engineering Department, Isfahan University of Technology, Isfahan, Iran

    Mohammad Sadegh Norouzzadeh, Mohammad Reza Ahmadzadeh & Maziar Palhang

Authors
  1. Mohammad Sadegh Norouzzadeh
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  2. Mohammad Reza Ahmadzadeh
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  3. Maziar Palhang
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Corresponding authors

Correspondence to Mohammad Sadegh Norouzzadeh or Mohammad Reza Ahmadzadeh.

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Norouzzadeh, M.S., Ahmadzadeh, M.R. & Palhang, M. LADPSO: using fuzzy logic to conduct PSO algorithm. Appl Intell 37, 290–304 (2012). https://doi.org/10.1007/s10489-011-0328-6

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  • DOI: https://doi.org/10.1007/s10489-011-0328-6

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