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A Review of Particle Swarm Optimization

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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.

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References

  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. 1942–1948

  2. K.E. Parsopoulos, M.N. Vrahatis, Recent approaches to global optimization problems through particle swarm optimization. J. Nat. Comput. 1, 235–306 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. K. Kameyama, Particle swarm optimization—a survey. Inst. Electron. Inf. Commun. Eng. E92-D, 1354–1361 (2009)

    Google Scholar 

  4. C.A. Floudas, C.E. Gounaris, A review of recent advances in global optimization. J. Global Optim. 45, 3–38 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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. 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/©

  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. K. Zielinski, R. Laum, Stopping criteria for a constrained single-objective particle swarm optimization algorithm. Informatica 31, 51–54 (2007)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  13. M.A. Abido, Optimal power flow using particle swarm optimization. Int. J. Electr. Power Energy Syst. 24(7), 563–571 (2002)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 1–6

  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. 1–7

  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)

    Article  Google Scholar 

  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)

    MathSciNet  Google Scholar 

  19. A. Nimtawat, P. Nanakom, Simple particle swarm optimization for solving beam-slab layout design problems. Elsevier 14, 1392–1398 (2011)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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: 1947-3931

  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. 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. 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. 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, 978-1-4673-4529-3/12

  29. P.J. Angeline, Using Selection to Improve Particle Swarm Optimization (Natural Selection Inc, Vestal) (1998), pp. 84–89

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 800–803

  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. 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. 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. A.M. Sharaf, A.A.A. Ei-Gammal, A Novel Discrete Multi-objective Particle Swarm Optimization (MOPSO) of Optimal Shunt Power Filter (IEEE, 2009), 978-1-4244-3811-2/09

  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)

    Article  MATH  Google Scholar 

  37. K.R. Harrison, B. Ombuki-Berman, A.P. Engelbrecht, Knowledge Transfer Strategies for Vector Evaluated Particle Swarm Optimization. Technical Report (Brock University, 2012)

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  43. J.F. Schutteand, A.A. Groenwold, A study of global optimization using particle swarms. J. Glob. Optim. 31, 93–108 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  44. W.-B. Liu, X.-J. Wang, An evolutionary game based particle swarm optimization algorithm. J. Comput. Appl. Math. 214, 30–35 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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 2079-8407

  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 ©

  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

    Article  MathSciNet  MATH  Google Scholar 

  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

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

  52. L. Baiqum, G. Gaiquin, L. Zeyu, The block diagram method for designing the particle swarm optimization. J. Glob. Optim. 52(689), 710 (2012)

    MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Electrical Engineering Department, Delhi Technological University, Delhi, 110042, India

    N. K. Jain, Uma Nangia & Jyoti Jain

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  1. N. K. Jain
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Jain, N.K., Nangia, U. & Jain, J. A Review of Particle Swarm Optimization. J. Inst. Eng. India Ser. B 99, 407–411 (2018). https://doi.org/10.1007/s40031-018-0323-y

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  • DOI: https://doi.org/10.1007/s40031-018-0323-y

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