Abstract
Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle’s position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Van den Bergh F, Engelbrecht A (2010) A convergence proof for the particle swarm optimizer. Fundamenta Informaticae 105(4):341–374
Blackwell T (2012) A study of collapse in bare bones particle swarm optimisation. IEEE Trans Evol Comput 16(3):354–375
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multi-dimensional complex space. IEEE Trans Evol Comput 6(1):58–73
Clerc M (2006) Particle swarm optimization. Wiley-ISTE Press, North America
Cooren Y, Clerc M, Siarry P (2011) MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm. Comput Optim Appl 49(2):379–400
Cristian TI (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85:317–325
Engelbrecht AP (2006) Fundamentals of computational swarm intelligence. Wiley-ISTE Press, North America
Gao H, Xu WB (2011) A new particle swarm algorithm and its globally convergent modifications. IEEE Trans Syst Man Cybern Part B Cybern 41(5):1334–1351
Haibo Z, Kennedy DD, Rangaiah GP, Bonilla-Petriciolet A (2011) Novel bare-bones particle swarm optimization and its performance for modeling vapor-liquid equilibrium data. Fluid Phase Equilib 301:33–45
Hu MQ, Wu T, Weir JD (2012) An intelligent augmentation of particle swarm optimization with multiple adaptive methods. Inf Sci 213:68–83
Jiang M, Luo YP, Yang SY (2007) Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Inf Process Lett 102(1):8–16
Kennedy J (2003) Bare bones particle swarms. In: Proceeding of the 2003 IEEE swarm intelligence symposium, pp 80–87
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference neural network, pp 1942–1948
Krohling Renato A, Mauro C, Patrick B (2010) Bare bones particle swarm applied to parameter estimation of mixed Weibull distribution. Adv Intell Soft Comput 75:53–60
Krohling RA, Mendel E (2009) Bare bones particle swarm optimization with Gaussian or Cauchy jumps. In: Proceedings of the IEEE international conference on evolutionary computation, pp 3285–3291
Mahamed GH, Omran Andries P, Salman EA (2009) Bare bones differential evolution. Eur J Oper Res 196:128–139
Majid al-Rifaie M, Blackwell T (2012) Bare bones particle swarms with jumps. Lect Notes Comput Sci 7461:49–60
Omran MGH, Engelbrecht A, Salman A (2007) Bare-bones particle swarm for integer programming problems. In: Proceeding of the IEEE swarm intelligence symposium, pp 170–175
Omran M, Al-Sharhan S (2007) Bare-bones particle swarm methods for unsupervised image classification. In: Proceeding of the IEEE congress on evolutionary computation, pp 3247–3252
Pan F, Hu X, Eberhart RC, Chen Y (2008) An analysis of bare bones particle swarm. In: Proceeding of the 2008 IEEE swarm intelligence symposium, pp 21–23
Poli R, Langdon WB (2007) Markov chain models of bare-bones particle swarm optimizers. In: Proceedings of the genetic and evolutionary computation conference (GECCO 2007), pp 142–149
Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceeding of the IEEE Congress on Evolutionary Computation, pp 303–308
Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the IEEE international conference on, evolutionary computation (CEC1999), pp 1945–1950
Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical Report for CEC2005 special session, 2005. http://www3.ntu.edu.sg/home/EPNSugan
Tripathi PK, Bandyopadhyay S, Pal SK (2007) Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Inf Sci 177(22):5033–5049
Wang L, Liu B (2008) Particle swarm optimization and scheduling algorithms. Tsinghua University Press, Beijing (in Chinese)
Wang HF, Ilkyeong M, Yang SX, Wang DW (2012) A memetic particle swarm optimization algorithm for multimodal optimization problems. Inf Sci 197:38–52
Yang ZY, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999
Zhang JQ, Ni LN, Yao J, Wang W, Tang Z (2011) Adaptive bare bones particle swarm inspired by cloud model. IEICE Trans Inf Syst E94-D(8):1527–1538
Zhang Y, Gong DW, Ding ZH (2012) A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch. Inf Sci 192(1):212–227
Acknowledgments
This work was supported by the Fundamental Research Funds for the Central Universities of China (2013QNA51).
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by Y. Jin.
Rights and permissions
About this article
Cite this article
Zhang, Y., Gong, Dw., Sun, Xy. et al. Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis. Soft Comput 18, 1337–1352 (2014). https://doi.org/10.1007/s00500-013-1147-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-013-1147-y