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Applied Intelligence

, Volume 49, Issue 2, pp 335–351 | Cite as

Enhanced particle swarm optimization with multi-swarm and multi-velocity for optimizing high-dimensional problems

  • Yong Ning
  • Zishun PengEmail author
  • Yuxing Dai
  • Daqiang Bi
  • Jun Wang
Article
  • 90 Downloads

Abstract

Traditional particle swarm optimization (PSO) algorithm mainly relies on the history optimal information to guide its optimization. However, when the traditional PSO algorithm searches high-dimensional complex problems, wrong position information of the best particles can easily cause the most of the particles move toward wrong space, so the traditional PSO algorithm is easily trapped into local optimum. To improve the optimization performance of the traditional PSO algorithm, an enhanced particle swarm optimization with multi-swarm and multi-velocity (MMPSO) is proposed. It comprises three particle swarms and three velocity update methods. The information sharing of the multi-swarm with various velocity update methods in the MMPSO can quickly discover more useful global information and local information, helping prevent particles from falling into local optimum and improving optimization precision of the algorithm. The MMPSO is tested on fourteen benchmark functions, and is compared with the other improved PSO algorithms. Comparison results validate the validity and feasibility of the MMPSO to optimize high-dimensional problems.

Keywords

Particle swarm optimization High-dimensional complex problems Information sharing Multi-swarm Multi-velocity 

References

  1. 1.
    Abualigah LM, Khader AT, Al-Betar MA, Alomari OA (2017) Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst Appl 84:24–36CrossRefGoogle Scholar
  2. 2.
    Alswaitti M, Albughdadi M, Isa NAM (2018) Density-based particle swarm optimization algorithm for data clustering. Expert Syst Appl 91:170–186CrossRefGoogle Scholar
  3. 3.
    Bamakan SMH, Wang H, Yingjie T, Shi Y (2016) An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization. Neurocomputing 199:90–102CrossRefGoogle Scholar
  4. 4.
    Chang WD (2017) Multimodal function optimizations with multiple maximums and multiple minimums using an improved PSO algorithm. Appl Soft Comput 60:60–72CrossRefGoogle Scholar
  5. 5.
    Chen J, Zheng J, Wu P, Zhang L, Wu Q (2017) Dynamic particle swarm optimizer with escaping prey for solving constrained non-convex and piecewise optimization problems. Expert Syst Appl 86:208–223CrossRefGoogle Scholar
  6. 6.
    Gülcü S, Kodaz H (2015) A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization. Eng Appl Artif Intell 45:33–45CrossRefGoogle Scholar
  7. 7.
    Gunasundari S, Janakiraman S, Meenambal S (2016) Velocity bounded boolean particle swarm optimization for improved feature selection in liver and kidney disease diagnosis. Expert Syst Appl 56:28–47CrossRefGoogle Scholar
  8. 8.
    Kadirkamanathan V, Selvarajah K, Fleming PJ (2006) Stability analysis of the particle dynamics in particle swarm optimizer. IEEE Trans Evol Comput 10(3):245–255CrossRefGoogle Scholar
  9. 9.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks proceedings, vol 4, pp 1942–1948Google Scholar
  10. 10.
    Kermadi M, Berkouk EM (2017) Artificial intelligence-based maximum power point tracking controllers for photovoltaic systems: comparative study. Renew Sust Energ Rev 69:369–386CrossRefGoogle Scholar
  11. 11.
    Khan SU, Yang S, Wang L, Liu L (2016) A modified particle swarm optimization algorithm for global optimizations of inverse problems. IEEE Trans Magn 52(3):1–4CrossRefGoogle Scholar
  12. 12.
    Kiranyaz S, Pulkkinen J, Gabbouj M (2011) Multi-dimensional particle swarm optimization in dynamic environments. Expert Syst Appl 38(3):2212–2223CrossRefGoogle Scholar
  13. 13.
    Kumar EV, Raaja GS, Jerome J (2016) Adaptive PSO for optimal LQR tracking control of 2 dof laboratory helicopter. Appl Soft Comput 41:77–90CrossRefGoogle Scholar
  14. 14.
    Li NJ, Wang WJ, Hsu CCJ, Chang W, Chou HG, Chang JW (2014) Enhanced particle swarm optimizer incorporating a weighted particle. Neurocomputing 124:218–227CrossRefGoogle Scholar
  15. 15.
    Li XM, Sun YL, Chen WN, Zhang J (2017) Multi-swarm particle swarm optimization for payment scheduling. In: 2017 seventh international conference on information science and technology (ICIST), pp 284–291Google Scholar
  16. 16.
    Liu R, Li J, fan J, Mu C, Jiao L (2017) A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization. Eur J Oper Res 261(3):1028–1051MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Liu ZG, Ji XH, Liu YX (2018) Hybrid non-parametric particle swarm optimization and its stability analysis. Expert Syst Appl 92:256–275CrossRefGoogle Scholar
  18. 18.
    Liu ZH, Wei HL, Zhong QC, Liu K, Li XH (2017) GPU implementation of DPSO-RE algorithm for parameters identification of surface PMSM considering VSI nonlinearity. IEEE J Emerg Select Topics Power Electron 5(3):1334–1345CrossRefGoogle Scholar
  19. 19.
    Ma K, Hu S, Yang J, Xu X, Guan X (2017) Appliances scheduling via cooperative multi-swarm PSO under day-ahead prices and photovoltaic generation. Appl Soft Comput 62:504–513CrossRefGoogle Scholar
  20. 20.
    Moradi MH, Bahrami FV, Mohammad A (2017) Power flow analysis in islanded micro-grids via modeling different operational modes of DGs: a review and a new approach. Renew Sust Energ Rev 69:248–262CrossRefGoogle Scholar
  21. 21.
    Nieto PG, Garcĺa-Gonzalo E, Fernández JA, Muñiz CD (2016) A hybrid PSO optimized SVM-based model for predicting a successful growth cycle of the spirulina platensis from raceway experiments data. J Comput Appl Math 291:293–303MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Pandit M, Srivastava L, Sharma M (2015) Performance comparison of enhanced PSO and DE variants for dynamic energy/reserve scheduling in multi-zone electricity market. Appl Soft Comput 37:619–631CrossRefGoogle Scholar
  23. 23.
    Rahmani M, Ghanbari A, Ettefagh MM (2016) Robust adaptive control of a bio-inspired robot manipulator using bat algorithm. Expert Syst Appl 56:164–176CrossRefGoogle Scholar
  24. 24.
    Samal NR, Konar A, Nagar A (2008) Stability analysis and parameter selection of a particle swarm optimizer in a dynamic environment. In: 2008 second UKSIM European symposium on computer modeling and simulation, pp 21–27Google Scholar
  25. 25.
    Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: IEEE international conference on evolutionary computation, pp 69–73Google Scholar
  26. 26.
    Shirani H, Habibi M, Besalatpour A, Esfandiarpour I (2015) Determining the features influencing physical quality of calcareous soils in a semiarid region of Iran using a hybrid PSO-DT algorithm. Geoderma 259-260:1–11CrossRefGoogle Scholar
  27. 27.
    Tanweer M, Suresh S, Sundararajan N (2015) Self regulating particle swarm optimization algorithm. Inf Sci 294:182–202MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    fang Wang Z, Wang J, mei Sui Q, Jia L (2017) The simultaneous measurement of temperature and mean strain based on the distorted spectra of half-encapsulated fiber bragg gratings using improved particle swarm optimization. Opt Commun 392:153–161CrossRefGoogle Scholar
  29. 29.
    Xu G (2013) An adaptive parameter tuning of particle swarm optimization algorithm. Appl Math Comput 219(9):4560–4569MathSciNetzbMATHGoogle Scholar
  30. 30.
    Yang C, Gao W, Liu N, Song C (2015) Low-discrepancy sequence initialized particle swarm optimization algorithm with high-order nonlinear time-varying inertia weight. Appl Soft Comput 29:386–394CrossRefGoogle Scholar
  31. 31.
    Yang G, Zhou F, Ma Y, Yu Z, Zhang Y, He J (2018) Identifying lightning channel-base current function parameters by powell particle swarm optimization method. IEEE Trans Electromagn Compat 60(1):182–187CrossRefGoogle Scholar
  32. 32.
    Yuan Q, Yin G (2015) Analyzing convergence and rates of convergence of particle swarm optimization algorithms using stochastic approximation methods. IEEE Trans Autom Control 60(7):1760–1773MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Electrical and Information EngineeringHunan UniversityChangshaChina
  2. 2.College of Physics and Electronic Information EngineeringWenzhou UniversityChangshaChina
  3. 3.State Key Laboratory of Power System, Department of Electrical EngineeringTsinghua UniversityBeijingChina

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