Advertisement

A Review of Particle Swarm Optimization

Review Paper
  • 99 Downloads

Abstract

This paper presents an overview of the research progress in Particle Swarm Optimization (PSO) during 1995–2017. Fifty two papers have been reviewed. They have been categorized into nine categories based on various aspects. This technique has attracted many researchers because of its simplicity which led to many improvements and modifications of the basic PSO. Some researchers carried out the hybridization of PSO with other evolutionary techniques. This paper discusses the progress of PSO, its improvements, modifications and applications.

Keywords

Iteration Optimization Particles PSO 

References

  1. 1.
    J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks (Perth, Australia) (IEEE Service Center, Piscataway, NJ, 1995), pp. 1942–1948Google Scholar
  2. 2.
    K.E. Parsopoulos, M.N. Vrahatis, Recent approaches to global optimization problems through particle swarm optimization. J. Nat. Comput. 1, 235–306 (2002)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    K. Kameyama, Particle swarm optimization—a survey. Inst. Electron. Inf. Commun. Eng. E92-D, 1354–1361 (2009)Google Scholar
  4. 4.
    C.A. Floudas, C.E. Gounaris, A review of recent advances in global optimization. J. Global Optim. 45, 3–38 (2009)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Y. Zhang, S. Wang, G. Ji, A comprehensive survey on particle swarm optimization algorithm and its applications, published in Hindawi, Math. Probl. Eng. 2015, 1–38 (2015)Google Scholar
  6. 6.
    Z. You, W. Chen, X. Nan, Adaptive weight Particle Swarm Optimization Algorithm with Constriction factor, in Proceedings of International Conference of Information Science and Management Engineering (2010), pp. 245–248.  https://doi.org/10.1109/isme.2010.234
  7. 7.
    J.C. Bansal, P.K. Singh, M. Saraswat, A. Verma, S.S. Jadon, A. Abraham, Inertia weight strategies in particle swarm optimization, in 2011 Third World Congress on Nature and Biologically inspired Computing (IEEE, 2011), pp. 633–640, 978-1-4577-1124-4/11/©Google Scholar
  8. 8.
    M.R. Bonyadi, Z. Michalewicz, Impacts of coefficients on movement patterns in the particle swarm optimization algorithm. IEEE Trans. Evolut. Comput. 21(3), 378–390 (2017)Google Scholar
  9. 9.
    K. Zielinski, R. Laum, Stopping criteria for a constrained single-objective particle swarm optimization algorithm. Informatica 31, 51–54 (2007)MATHGoogle Scholar
  10. 10.
    Q. Wu, C. Cole, T. McSweeng, Applications of particle swarm optimization in the railway domain. Int. J. Rail Transp. 4(3), 167–190 (2016)CrossRefGoogle Scholar
  11. 11.
    M.R. Al Rashidi, M.E. El-Hawary, A survey of particle swarm optimization applications in electric power systems. IEEE Trans. Evolut. Comput. 13(4), 913–918 (2016)CrossRefGoogle Scholar
  12. 12.
    N.K. Jain, U. Nangia, A. Jain, PSO for multiobjective economic load dispatch (MELD) for minimizing generation cost and transmission losses. J. Inst. Eng. (India) Ser. B 97(2), 185–191 (2016)CrossRefGoogle Scholar
  13. 13.
    M.A. Abido, Optimal power flow using particle swarm optimization. Int. J. Electr. Power Energy Syst. 24(7), 563–571 (2002)CrossRefGoogle Scholar
  14. 14.
    R.-H. Liang, R.-H. Liang, Y.-T. Chen, W.-T. Tseng, Optimal power flow by a fuzzy based hybrid particle swarm optimization approach. Electr. Power Syst. Res. 81(7), 1466–1474 (2011)CrossRefGoogle Scholar
  15. 15.
    C.P. Salomon, G. Lambert-Torres, H.G. Martins, C. Ferreira, C.I.A., Costa Load flow computation via particle swarm optimization, in 9th IEEE/IAS International Conference on Industry Applications (INDUSCON) (2010), 8–10 Nov 2010, pp. 1–6Google Scholar
  16. 16.
    P. Acharjee, S.K. Goswami, Chaotic particle swarm optimization based reliable algorithm to overcome the limitations of conventional power flow methods, in Power Systems Conference and Exposition, 2009. PSCE ‘09. IEEE/PES, 15–18 March 2009, pp. 1–7Google Scholar
  17. 17.
    Z.L. Gaing, A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans. Energy Convers. 19(2), 384–391 (2004)CrossRefGoogle Scholar
  18. 18.
    H. YapJcJ, N. Çetinkaya, An improved particle swarm optimization algorithm using eagle strategy for power loss minimization. Math. Probl. Eng. (2017).  https://doi.org/10.1155/2017/1063045. (Article ID 1063045) MathSciNetGoogle Scholar
  19. 19.
    A. Nimtawat, P. Nanakom, Simple particle swarm optimization for solving beam-slab layout design problems. Elsevier 14, 1392–1398 (2011)Google Scholar
  20. 20.
    T.T. Mac, C. Copot, D.T. Tran, R. De Keyser, A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. Appl. Soft Comput. 59, 68–76 (2017)CrossRefGoogle Scholar
  21. 21.
    M.J. Islam, X. Li, Y. Mei, A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO. Appl. Soft Comput. 59, 182–196 (2017)CrossRefGoogle Scholar
  22. 22.
    A. Suresha, K.V. Harisha, N. Radhika, Particle swarm optimization over back propagation neural network for length of stay prediction. Procedia Comput. Sci. 46, 268–275 (2015)CrossRefGoogle Scholar
  23. 23.
    R. Zoi, V. Kalivarapu, E. Winer, J. Oliver, S. Bhattacharya, Particle swarm optimization based source seeking. IEEE Trans. Autom. Sci. Eng. 12(3), 865–875 (2015)CrossRefGoogle Scholar
  24. 24.
    P. Wen, M. Zhi, G. Zhang, S. Li, Fault prediction of elevator door system based on PSO-BP neural network, Scientific Research Publishing, Engineering 8, 761–766 (2016). ISSN Online: 1947-394X, ISSN Print: 1947-3931Google Scholar
  25. 25.
    T. Gong, A.L. Tuson, Particle swarm optimization for quadratic assignment problems—a forma analysis approach. Int. J. Comput. Intell. Res. 2, 1–9 (2007)Google Scholar
  26. 26.
    Z. Liu, R. Zhao, Equipment possession quantity modelling and particle swarm optimization, in Proceedings of Third IEEE International Conference on Genetic Evolutionary Computing (2009), pp. 628–632.  https://doi.org/10.1109/wgec
  27. 27.
    J.-Q. Li, Q.-K. Pank, B.-X. Jia, Y.-T. Wang, A hybrid particle swarm optimization and tabu search algorithm for flexible job-shop scheduling problem. Int. J. Comput. Theory Eng. 2(2), 1793–8201 (2010)Google Scholar
  28. 28.
    B. Bhushan, S.S. Pillai, Particle swarm optimization and firefly algorithm: performance analysis, in 2013 3rd IEEE International Advance Computing Conference (IACC) (IEEE, 2013), pp. 746–751, 978-1-4673-4529-3/12Google Scholar
  29. 29.
    P.J. Angeline, Using Selection to Improve Particle Swarm Optimization (Natural Selection Inc, Vestal) (1998), pp. 84–89Google Scholar
  30. 30.
    Y.-P. Chen, W.-C. Peng, Particle swarm optimization with recombination and dynamic linkage discovery. IEEE Trans. Syst. Man Cybern. Part B Cybern. 37(6), 1460–1470 (2007)CrossRefGoogle Scholar
  31. 31.
    W. Jaio, G. Liu, D. Liu, Elite particle swarm optimization with mutation, in 2008 Asia simulation Conference—Proceedings of IEEE 7th International Conference on Systems Simulation and Scientific Computing (2008), pp. 800–803Google Scholar
  32. 32.
    S. Song, Shujun et al., Improved particle swarm cooperative optimization algorithm based on chaos & simplex method, in Proceedings o f Second IEEE International Workshop on Education Technology and Computer Science (2010).  https://doi.org/10.1109/etcs.2010.235.10
  33. 33.
    M. Chen, T. Wang, J. Feng, Y.-Y. Tang, L.-X. Zhao, A hybrid particle swarm optimization improved by mutative scale chaos algorithm, in Fourth International Conference on Computational and information Sciences (IEEE, 2012), pp. 321–324, 978-0-7695-4789-3/12 ©.  https://doi.org/10.1109/iccis.2012.19
  34. 34.
    J. Liu, B. Zhu, The application of particle swarm optimization algorithm in the extremum optimization of nonlinear function, in 12th IEEE International Conference on Computer and Information Technology (IEEE, 2012), pp. 286–289,978-0-7695-4858-6/12 ©.  https://doi.org/10.1109/cit.2012.74
  35. 35.
    A.M. Sharaf, A.A.A. Ei-Gammal, A Novel Discrete Multi-objective Particle Swarm Optimization (MOPSO) of Optimal Shunt Power Filter (IEEE, 2009), 978-1-4244-3811-2/09Google Scholar
  36. 36.
    C.K. Goh, K.C. Tan, D.S. Liu, S.C. Chaim, A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design. Eur. J. Oper. Res. 202, 42–54 (2010)CrossRefMATHGoogle Scholar
  37. 37.
    K.R. Harrison, B. Ombuki-Berman, A.P. Engelbrecht, Knowledge Transfer Strategies for Vector Evaluated Particle Swarm Optimization. Technical Report (Brock University, 2012)Google Scholar
  38. 38.
    M. Benedetti, A. Massa, Memory enhanced PSO-based optimization approach for smart antennas control in complex interference scenarios. IEEE Trans. Antennas Prop. Mag. 56(7), 1939–1947 (2008)CrossRefGoogle Scholar
  39. 39.
    H. Duan, P. Li, Y. Yu, A predator-prey Particle swarm optimization approach to multiple UCAV air combat modeled by dynamic game theory. IEEE/CAA J. Autom. Sin. 2(1), 11–18 (2015)MathSciNetCrossRefGoogle Scholar
  40. 40.
    J.J. Liang, A.K. Qin, P.N. Suganthan, S. Baskar, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evolut. Comput. 10(3), 281–295 (2006)CrossRefGoogle Scholar
  41. 41.
    C. Li, S. Yang, T.T. Nguyen, A self-learning particle swarm optimizer for global optimization problems. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(3), 627–646 (2012)CrossRefGoogle Scholar
  42. 42.
    Z.-H. Zhan, J. Zhang, Y. Li, Y.-H. Shi, Orthogonal learning particle swarm optimization. IEEE Trans. Evolut. Comput. 15(6), 832–847 (2011)CrossRefGoogle Scholar
  43. 43.
    J.F. Schutteand, A.A. Groenwold, A study of global optimization using particle swarms. J. Glob. Optim. 31, 93–108 (2005)MathSciNetCrossRefMATHGoogle Scholar
  44. 44.
    W.-B. Liu, X.-J. Wang, An evolutionary game based particle swarm optimization algorithm. J. Comput. Appl. Math. 214, 30–35 (2008)MathSciNetCrossRefMATHGoogle Scholar
  45. 45.
    S. Hossen, F. Rabbi, M. Rahman, Adaptive particle swarm optimization (APSO) for multimodal function optimization. Int. J. Eng. Technol. 1(3), 98–103 (2009)Google Scholar
  46. 46.
    B. Benmessahel, M. Touahria, An improved combinatorial particle swarm optimization algorithm to database verticle partition. J. Emerg. Trends Comput. Inf. Sci. 2(3), 130–135 (2010), ISSN 2079-8407Google Scholar
  47. 47.
    W. Jii, K. Wangi, An improved particle swarm optimization algorithm, in 2011 International Conference on Computer Science and Network Technology (IEEE, 2011), pp. 585–589, 978-1-4577-1587-7111/$26.00 ©Google Scholar
  48. 48.
    Z. Beheshti, S.M. Shamsuddin, S.S. Yuhaniz, Binary accelerated particle swarm algorithm (BAPSA) for discrete optimization problems. J. Glob. Optim. 57, 549–573 (2013).  https://doi.org/10.1007/s10898-012-0006-1 MathSciNetCrossRefMATHGoogle Scholar
  49. 49.
    L.M. Rios, N.V. Sahinidis, Derivative-free optimization: a review of algorithms and comparison of software implementations. J. Glob. Optim. 56, 1247–1293 (2013).  https://doi.org/10.1007/s10898-012-9951-y MathSciNetCrossRefMATHGoogle Scholar
  50. 50.
    Z. Chen, Y. Bo, P. Wu, W. Zhou, A new particle filter based on organizational adjustment particle swarm optimization. Appl. Math. Inf. Sci. 7(1), 179–186 (2013)MathSciNetCrossRefGoogle Scholar
  51. 51.
    M.A. Arasomwan, A.O. Adewumi, An Adaptive Velocity Particle Swarm Optimization for High-Dimensional Function Optimization Congress on Evolutionary Computation, June 20–23, Cancún, México (IEEE, 2013)Google Scholar
  52. 52.
    L. Baiqum, G. Gaiquin, L. Zeyu, The block diagram method for designing the particle swarm optimization. J. Glob. Optim. 52(689), 710 (2012)MathSciNetGoogle Scholar

Copyright information

© The Institution of Engineers (India) 2018

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentDelhi Technological UniversityDelhiIndia

Personalised recommendations