Self balanced particle swarm optimization

Original Article
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Abstract

In the field of swarm intelligence inspired algorithms, particle swarm optimization (PSO) is a renowned meta-heuristic due to its simplicity, performance, and implementation. However, the PSO also have some downsides like stagnation and slow convergence due to improper balance between the diversification and convergence abilities of the population. Therefore, in this paper, solution search process of PSO algorithm is modified to balance the organization of the individuals in the search space. In the proposed approach, artificial bee colony (ABC) algorithm inspired fitness-based solution search process is incorporated with the PSO algorithm. The proposed approach is tested over 20 unbiased benchmark functions, and the reported results are compared with PSO 2011, ABC, differential evaluation, self-adaptive acceleration factor in PSO, and Mean PSO algorithms through proper statistical analyses.

Keywords

Population based algorithm Swarm intelligence Nature inspired algorithm Optimization 

References

  1. Angeline P (1998) Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. In: Proceedings of evolutionary programming VII, Springer, pp 601–610Google Scholar
  2. Bansal Jagdish Chand, Sharma Harish, Jadon Shimpi Singh, Clerc Maurice (2014) Spider monkey optimization algorithm for numerical optimization. Memet Comput 6(1):31–47CrossRefGoogle Scholar
  3. Bansal J, Sharma H, Arya K (2012) Model order reduction of single input single output systems using artificial bee colony optimization algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2011), pp 85–100Google Scholar
  4. Bansal JC, Sharma H (2011) Model order reduction of single input single output systems using artificial bee colony optimization algorithm. In: Studies in computational intelligence, NICSO 2011, Springer, p 387Google Scholar
  5. Ciuprina G, Ioan D, Munteanu I (2002) Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans Magn 38(2):1037–1040CrossRefGoogle Scholar
  6. Deep Kusum, Bansal Jagdish Chand (2009) Mean particle swarm optimisation for function optimisation. Int J Comput Intell Stud 1(1):72–92CrossRefGoogle Scholar
  7. Diwold K, Aderhold A, Scheidler A, Middendorf M (2011) Performance evaluation of artificial bee colony optimization and new selection schemes. Memet Comput 1(1):1–14MATHGoogle Scholar
  8. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the 6th international symposium on micro machine and human science, MHS’95, IEEE, pp 39–43Google Scholar
  9. El-Abd M (2011) Performance assessment of foraging algorithms vs. evolutionary algorithms. Inf Sci 182(1):243–263MathSciNetCrossRefGoogle Scholar
  10. Gai-yun W, Dong-xue H (2009) Particle swarm optimization based on self-adaptive acceleration factors. In: 3rd International conference on genetic and evolutionary computing, WGEC’09, IEEE, pp 637–640Google Scholar
  11. Jadon SS, Sharma H, Bansal JC, Tiwari R (2013) Self adaptive acceleration factor in particle swarm optimization. In: Proceedings of 7th international conference on bio-inspired computing: theories and applications (BIC-TA 2012), Springer, pp 325–340Google Scholar
  12. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report, TR06, Erciyes University Press, ErciyesGoogle Scholar
  13. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, 1995, vol 4, IEEE, pp 1942–1948Google Scholar
  14. Kim JJ, Park SY, Lee JJ (2009) Experience repository based particle swarm optimization for evolutionary robotics. In: ICCAS-SICE, 2009, IEEE, pp 2540–2544Google Scholar
  15. Li XD, Engelbrecht AP (2007) Particle swarm optimization: an introduction and its recent developments. Genet Evol Comput Conf, pp 3391–3414Google Scholar
  16. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295CrossRefGoogle Scholar
  17. Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18(1):50–60MathSciNetCrossRefMATHGoogle Scholar
  18. Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255CrossRefGoogle Scholar
  19. Sharma H, Bansal JC, Arya KV (2012a) Fitness based differential evolution. Memet Comput 4:303–316. doi: 10.1007/s12293-012-0096-9 CrossRefGoogle Scholar
  20. Sharma H, Bansal J, Arya K (2012b) Dynamic scaling factor based differential evolution algorithm. In: Proceedings of the international conference on soft computing for problem solving (SocProS 2011), Springer, 20–22 December 2011, pp 73–85Google Scholar
  21. Sharma H, Verma A, Bansal J (2012c) Group social learning in artificial bee colony optimization algorithm. In: Proceedings of the international conference on soft computing for problem solving (SocProS 2011), Springer, 20–22 December 2011, pp 441–451Google Scholar
  22. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of the IEEE international conference on evolutionary computation, IEEE world congress on computational intelligence, 1998, IEEE, pp 69–73Google Scholar
  23. Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. In: Proceedings of the evolutionary programming VII, Springer, pp 591–600Google Scholar
  24. Storn R, Price K (1995) Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science Institute, Publications, TRGoogle Scholar
  25. Zhan ZH, Zhang J, Li Y, Chung HSH (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B: Cybern 39(6):1362–1381CrossRefGoogle Scholar
  26. Zhang W, Li H, Zhang Z, Wang H (2008) The selection of acceleration factors for improving stability of particle swarm optimization. In: 4th International conference on natural computation, ICNC’08, 2008, vol 1, IEEE, pp 376–380Google Scholar

Copyright information

© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017

Authors and Affiliations

  1. 1.Arya College of Engineering & ITJaipurIndia
  2. 2.Jagannath UniversityJaipurIndia
  3. 3.Government Polytechnic CollegeKotaIndia

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