Skip to main content
SpringerLink
Log in

An improved particle swarm optimization algorithm based on comparative judgment

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

Particle swarm optimization (PSO) algorithm is one of the most effective and popular swarm intelligence algorithms. In this paper, based on comparative judgment, an improved particle swarm optimization (IPSO) is proposed. Firstly, a new search equation is developed by considering individual experience, social experience and the integration of individual and social experience, which can be used to improve the convergence speed of the algorithm. Secondly, in order to avoid falling into a local optima, a location abandoned mechanism is proposed; meanwhile, a new equation to generate a new position for the corresponding particle is proposed. The experimental results show that IPSO algorithm has excellent solution quality and convergence characteristic comparing to basic PSO algorithm and performs better than some state-of-the-art algorithms on almost all tested functions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Alatas B, Akin E, Ozer AB (2009) Chaos embedded particle swarm optimization algorithms. Chaos Solitons Fractals 40(4):1715–1734

    Article  MathSciNet  MATH  Google Scholar 

  • Ardizzon G, Cavazzini G, Pavesi G (2015) Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms. Inf Sci 299:337–378

    Article  Google Scholar 

  • Beheshti Z, Shamsuddin SMH (2014) CAPSO: centripetal accelerated particle swarm optimization. Inf Sci 258:54–79

    Article  MathSciNet  Google Scholar 

  • Deep K, Thakur M (2007) A new mutation operator for real coded genetic algorithms. Appl Math Comput 193:211–230

    MathSciNet  MATH  Google Scholar 

  • Eslami M, Shareef H et al (2012) A survey of the state of the art in particle swarm optimization. Res J Appl Sci Eng Technol 4(9):1181–1197

    Google Scholar 

  • Esmin AA, Matwin S (2013) HPSOM: a hybrid particle swarm optimization algorithm with genetic mutation. Int J Innov Comput Inf Control 9(5):1919–1934

    Google Scholar 

  • Gabere N (2007) Simulated annealing driven pattern search algorithms for global optimization, Masters thesis. University of the Witwatersrand, Johannesburg, South Africa

    Google Scholar 

  • Gao WF, Liu SY, Huang LL (2012) Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique. Commun Nonlinear Sci Numer Simul 7(11):4316–4327

    Article  MathSciNet  MATH  Google Scholar 

  • Hakli H, Uguz H (2014) A novel particle swarm optimization algorithm with levy flight. Appl Soft Comput 23(5):333–345

    Article  Google Scholar 

  • Hu MQ, Wu T, Weir JD (2013) An adaptive particle swarm optimization with multiple adaptive methods. IEEE Trans Evol Comput 17(5):705–720

    Article  Google Scholar 

  • Jiao B, Lian ZG, Gu XS (2008) A dynamic inertia weight particle swarm optimization algorithm. Chaos, Solitons Fractals 37(3):698–705

    Article  MATH  Google Scholar 

  • Jordehi AR, Jasni J (2013) Parameter selection in particle swarm optimisation: a survey. J Exp Theor Artif Intell 25(4):527–542

    Article  Google Scholar 

  • Karaboga D, Gorkemli B et al (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948

    Google Scholar 

  • 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):281–295

    Article  Google Scholar 

  • Liang JJ, Qin A, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10:281–295

    Article  Google Scholar 

  • Liao TJ, Stützle T et al (2014) A unified ant colony optimization algorithm for continuous optimization. Eur J Oper Res 234(3):597–609

    Article  MathSciNet  MATH  Google Scholar 

  • Lim WH, Isa NAM (2014) An adaptive two-layer particle swarm optimization with elitist learning strategy. Inf Sci 273:49–72

    Article  MathSciNet  Google Scholar 

  • Lu H, Joarder K (2014) A modified immune network optimization algorithm. IAENG Int J Comput Sci 41(4):231–236

    Google Scholar 

  • Manjarresa D, Landa-Torresa I (2013) A survey on applications of the harmony search algorithm. Eng Appl Artif Intell 26(8):1818–1831

    Article  Google Scholar 

  • Manuel L, Manuel L et al (2016) A genetic algorithm for the minimum generating set problem. Appl Soft Comput 48:254–264

    Article  Google Scholar 

  • Medjahed SA, Saadi TA, Benyettou A et al (2015) Binary cuckoo search algorithm for band selection in hyperspectral image classification. IAENG Int J Comput Sci 42(3):183–191

    Google Scholar 

  • Mendes R, Kennedy J , Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210

    Article  Google Scholar 

  • Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput 11(4):3658–3670

    Article  Google Scholar 

  • Noel MM (2012) A new gradient based particle swarm optimization algorithm for accurate computation of global minimum. Appl Soft Comput 12(1):353–359

    Article  Google Scholar 

  • Parrott D, Li X (2006) Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans Evol Comput 10(4):440–458

    Article  Google Scholar 

  • Rapaić MR, Kanović Z (2009) Time-varying PSO C convergence analysis, convergence-related parameterization and new parameter adjustment schemes. Inf Process Lett 109(11):548–552

    Article  MathSciNet  MATH  Google Scholar 

  • Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255

    Article  Google Scholar 

  • Shin YB, Kita E (2014) Search performance improvement of particle swarm optimization by second best particle information. Appl Math Comput 246:346–354

    MathSciNet  MATH  Google Scholar 

  • Suganthan PN, Hansen N, Liang JJ (2005) Problem definitions and evaluation criteria for the CEC special session on real-parameter optimization. Technical report: Nanyang Technological University and KanGAL report 2005005:2005

  • Taherkhani M, Safabakhsh R (2016) A novel stability-based adaptive inertia weight for particle swarm optimization. Appl Soft Comput 38:281–295

    Article  Google Scholar 

  • Wang CF, Liu K (2016) A novel particle swarm optimization algorithm for global optimization. Comput Intell Neurosci 1–9:2016

    Google Scholar 

  • Wang H, Wu Z, Rahnamayan S et al (2011) Particle swarm optimization with simple and efficient neighbourhood search strategies. Int J Innov Comput Appl 3(2):97–104

    Article  Google Scholar 

  • Wang H, Sun H, Li C et al (2013) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci 223:119–135

    Article  MathSciNet  Google Scholar 

  • Wu GH, Qiu DS, Yu Q et al (2014) Superior solution guided particle swarm optimization combined with local search techniques. Expert Syst Appl 41:7536–7548

    Article  Google Scholar 

  • Yang XM, Yuan JS et al (2007) A modified particle swarm optimizer with dynamic adaptation. Appl Math Comput 189(2):1205–1213

    MathSciNet  MATH  Google Scholar 

  • Yazdani D, Nasiri B, Alireza SM, Meybodi MR (2013) A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization. Appl Soft Comput 13(4):77–93

    Article  Google Scholar 

  • Zhan ZH, Zhang J et al (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern 39(6):1362–1381

    Article  Google Scholar 

  • Zhan ZH, Zhang J et al (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B Cybern 39:1362–1381

    Article  Google Scholar 

  • Zhan ZH, Zhang J et al (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847

    Article  Google Scholar 

  • Zhang Y, Gong DW et al (2014) Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis. Soft Comput 18(7):1337–1352

    Article  MATH  Google Scholar 

  • Zhang D, Guan Z, Liu X (2008) Adaptive particle swarm optimization algorithm with dynamically changing inertia weight. Control and Decision 11:1253–1257

    MATH  Google Scholar 

  • Zhou XG, Zhang GJ, Hao XH (2016) A novel differential evolution algorithm using local abstract convex underestimate strategy for global optimization. Comput Oper Res 75:132–149

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The research was supported by NSFC(U1404105, 11671122); the Key Scientific and Technological Project of Henan Province(142102210058);the Doctoral Scientific Research Foundation of Henan Normal University(qd12103);the Youth Science Foundation of Henan Normal University(2013qk02);Henan Normal University National Research Project to Cultivate the Funded Projects (01016400105); the Henan Normal University Youth Backbone Teacher Training; the Key Project of Henan Educational Committee(16A510006). The authors would like to appreciate for reviewing our paper and thank two anonymous referees for their valuable comments that improved quality of the paper.

Author information

Authors and Affiliations

  1. Department of Mathematics, Henan Normal University, Xinxiang, 453007, People’s Republic of China

    Chun-Feng Wang & Kui Liu

Authors
  1. Chun-Feng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chun-Feng Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, CF., Liu, K. An improved particle swarm optimization algorithm based on comparative judgment. Nat Comput 17, 641–661 (2018). https://doi.org/10.1007/s11047-017-9630-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-017-9630-5

Keywords

Search

Navigation