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A Survey on Parallel Particle Swarm Optimization Algorithms

  • Soniya LalwaniEmail author
  • Harish Sharma
  • Suresh Chandra Satapathy
  • Kusum Deep
  • Jagdish Chand Bansal
Review - Computer Engineering and Computer Science
  • 40 Downloads

Abstract

Most of the complex research problems can be formulated as optimization problems. Emergence of big data technologies have also commenced the generation of complex optimization problems with large size. The high computational cost of these problems has rendered the development of optimization algorithms with parallelization. Particle swarm optimization (PSO) algorithm is one of the most popular swarm intelligence-based algorithm, which is enriched with robustness, simplicity and global search capabilities. However, one of the major hindrance with PSO is its susceptibility of getting entrapped in local optima and; alike other evolutionary algorithms the performance of PSO gets deteriorated as soon as the dimension of the problem increases. Hence, several efforts are made to enhance its performance that includes the parallelization of PSO. The basic architecture of PSO inherits a natural parallelism, and receptiveness of fast processing machines has made this task pretty convenient. Therefore, parallelized PSO (PPSO) has emerged as a well-accepted algorithm by the research community. Several studies have been performed on parallelizing PSO algorithm so far. Proposed work presents a comprehensive and systematic survey of the studies on PPSO algorithms and variants along with their parallelization strategies and applications.

Keywords

Particle swarm optimization Parallel computing Swarm intelligence-based algorithm GPU MPI Large-size complex optimization problems 

Abbreviations

AWS

Amazon web services

CA

Cellular automata

CNOP

Conditional nonlinear optimal perturbation

CPU

Central processing unit

CUDA

Compute unified device architecture

DNN

Deep neural networks

DORPD

Dynamic optimal reactive power dispatch

ED

Economic dispatch

FJSP

Flexible job shop scheduling problem

FPGA

Field programmable gate array

GA

Genetic algorithm

GPU

Graphics processing unit

HPF

High-performance Fortran

HSI

Hyper spectral images

JSSP

Job shop scheduling problem

MOP

Multi-objective optimization problem

MPI

Message-passing interface

NMR

Nuclear magnetic resonance

OpenCL

Open computing language

OpenGL

Open graphics library

OpenMP

Open multiprocessing

PPSO

Parallel particle swarm optimization

PSO

Particle swarm optimization

PVM

Parallel virtual machine

QoS

Quality of service

SA

Simulated annealing

SMP

Symmetric multiprocessing

TPU

Tensor processing unit

TSVD

Truncated singular value decomposition

UAV

Unmanned aerial vehicle

V2G

Vehicle-to-grid

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Notes

Acknowledgements

The first author (S.L.) gratefully acknowledges Science & Engineering Research Board, DST, Government of India, for the fellowship (PDF/2016/000008).

References

  1. 1.
    Bergh, V.: An Analysis of Particle Swarm Optimizers. Ph.D. thesis, Faculty of Natural and Agricultural Science, University of Pretoria (2001)Google Scholar
  2. 2.
    Kennedy, J.F.; Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
  3. 3.
    Umbarkar, A.J.; Joshi, M.S.: Review of parallel genetic algorithm based on computing paradigm and diversity in search space. ICTACT J. Soft Comput. 3(4), 615–622 (2013)Google Scholar
  4. 4.
    Cao, B.; Zhao, J.; Zhihan, L.; Liu, X.; Yang, S.; Kang, X.; Kang, K.: Distributed parallel particle swarm optimization for multi-objective and many-objective large-scale optimization. IEEE Access Spec. Sect. Big Data Anal. Internet Things Cyber-Phys. Syst. 5, 8214–8221 (2017)Google Scholar
  5. 5.
    Lalwani, S.; Kumar, R.; Gupta, N.: A novel two-level particle swarm optimization approach to train the transformational grammar based hidden Markov models for performing structural alignment of pseudoknotted RNA. Swarm Evolut. Comput. 20, 58–73 (2015)Google Scholar
  6. 6.
    Selvi, S.; Manimegalai, D.: Task scheduling using two-phase variable neighborhood search algorithm on heterogeneous computing and grid environments. Arab. J. Sci. Eng. 40(3), 817–844 (2015)zbMATHGoogle Scholar
  7. 7.
    Fernandez-Villaverdey, J.; Zarruk-Valenciaz, D.: A Practical Guide to Parallelization in Economics. University of Pennsylvania, Philadelphia (2018)Google Scholar
  8. 8.
    The Apache Software Foundation. Apache Hadoop. http://hadoop.apache.org/ (2018)
  9. 9.
    MATLAB and Simulink. https://in.mathworks.com/ (2018)
  10. 10.
    Wickham, H.: Advanced R. Chapman and Hall/CRC The R Series. Taylor and Francis, Milton Park (2014)Google Scholar
  11. 11.
    The Julia Language. https://docs.julialang.org/en/stable/manual/parallel-computing. Julia Parallel Computing (2018)
  12. 12.
    Gorelick, M.; Ozsvald, I.: High Performance Python: Practical Performant Programming for Humans. O’Reilly Media, Sebastopol (2014)Google Scholar
  13. 13.
    Bjarne Stroustrup.: Past, present and future of C++. http://cppcast.com/2017/05/bjarne-stroustrup/ (2017)
  14. 14.
    The OpenMP API specification for parallel programming. http://www.openmp.org/ (2018)
  15. 15.
    Gropp, W.; Lusk, E.; Skjellum, A.: Using MPI: Portable Parallel Programming with the Message-Passing Interface, vol. 1. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  16. 16.
    nVIDIA.: nVIDIA CUDA Programming Guide v.2.3. nVIDIA Corporation, Santa Clara (2009)Google Scholar
  17. 17.
    Mei, G.; Tipper, J.C.; Xu, N.: A generic paradigm for accelerating laplacian-based mesh smoothing on the GPU. Arab. J. Sci. Eng. 39(11), 7907–7921 (2014)Google Scholar
  18. 18.
    Farber, R.: Parallel Programming with OpenACC. Morgan Kaufmann, Burlington (2017)Google Scholar
  19. 19.
    Kaz, S.: An in-depth look at Google’first Tensor Processing Unit. https://cloud.google.com/blog/bigdata /2017/05/an-in-depth-look-at-googles-first-tensor-processing-unit-TPU (2018)
  20. 20.
    Zou, X.; Wang, L.; Tang, Y.; Liu, Y.; Zhan, S.; Tao, F.: Parallel design of intelligent optimization algorithm based on FPGA. Int. J. Adv. Manuf. Technol. 94(9), 3399–3412 (2018)Google Scholar
  21. 21.
    Cantu-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Norwell (2000)zbMATHGoogle Scholar
  22. 22.
    Madhuri, A., Deep, K.: A state-of-the-art review of population-based parallel meta-heuristics. In: World Congress on Nature and Biologically Inspired Computing, pp. 1604–1607 (2009)Google Scholar
  23. 23.
    Gies, D.; Rahmat-Samii, Y.: Reconfigurable array design using parallel particle swarm optimization. IEEE Int. Symp. Antennas Propag. Soc. 1, 177–180 (2003)Google Scholar
  24. 24.
    Schutte, J.F.; Fregly, B.J.; Haftka, R.T.; George, A.D.: A parallel particle swarm optimizer. Technical report, Florida University, Gainesville Mechanical and Aerospace Engineering (2003)Google Scholar
  25. 25.
    Schutte, J.F.; Reinbolt, J.A.; Fregly, B.J.; Haftka, R.T.; George, A.D.: Parallel global optimization with the particle swarm algorithm. Int. J. Numer. Methods Eng. 61(13), 2296–2315 (2004)zbMATHGoogle Scholar
  26. 26.
    Cui, S.; Weile, D.S.: Application of a parallel particle swarm optimization scheme to the design of electromagnetic absorbers. IEEE Trans. Antennas Propag. 53(11), 3616–3624 (2005)Google Scholar
  27. 27.
    Venter, G.; Sobieszczanski-Sobieski, J.: Parallel particle swarm optimization algorithm accelerated by asynchronous evaluations. J. Aerosp. Comput. Inf. Commun. 3(3), 123–137 (2006)Google Scholar
  28. 28.
    Chusanapiputt, S.; Nualhong, D.; Jantarang, S.; Phoomvuthisarn, S.: Relative velocity updating in parallel particle swarm optimization based lagrangian relaxation for large-scale unit commitment problem. In: IEEE Region 10 Conference, Melbourne, Qld., Australia, pp. 1–6 (2005)Google Scholar
  29. 29.
    Koh, B.-I.; George, A.D.; Haftka, R.T.; Fregly, B.J.: Parallel asynchronous particle swarm optimization. Int. J. Numer. Methods Eng. 67(4), 578–595 (2006)zbMATHGoogle Scholar
  30. 30.
    McNabb, A.W.; Monson, C.K.; Seppi, K.D.: Parallel PSO using MapReduce. In: IEEE Congress on Evolutionary Computation, pp. 7–14 (2007)Google Scholar
  31. 31.
    Liu, Q.; Li, T.; Liu, Q.; Zhu, J.; Ding, X.; Wu, J.: Two phase parallel particle swarm algorithm based on regional and social study of object optimization. In: Third IEEE International Conference on Natural Computation, vol. 3, pp. 827–831 (2007)Google Scholar
  32. 32.
    Han, F.; Cui, W.; Wei, G.; Wu, S.: Application of parallel PSO algorithm to motion parameter estimation. In: 9th IEEE International Conference on Signal Processing, pp. 2493–2496 (2008)Google Scholar
  33. 33.
    Wang, D.; Wu, C.H.; Ip, A.; Wang, D.; Yan, Y.: Parallel multi-population particle swarm optimization algorithm for the uncapacitated facility location problem using openMP. In: IEEE World Congress on Computational Intelligence Evolutionary Computation, pp. 1214–1218 (2008)Google Scholar
  34. 34.
    Jeong, H.M.; Lee, H.S.; Park, J.H.: Application of parallel particle swarm optimization on power system state estimation. In: Transmission and Distribution Conference and Exposition: Asia and Pacific, pp. 1–4 (2009)Google Scholar
  35. 35.
    Lihua, C.; Yadong, M.; Na, Y.: Parallel particle swarm optimization algorithm and its application in the optimal operation of cascade reservoirs in Yalong river. In: Second IEEE International Conference on Intelligent Computation Technology and Automation vol. 1, pp. 279–282 (2009)Google Scholar
  36. 36.
    Kalivarapu, V.; Foo, J.L.; Winer, E.: Synchronous parallelization of particle swarm optimization with digital pheromones. Adv. Eng. Softw. 40(10), 975–985 (2009)zbMATHGoogle Scholar
  37. 37.
    Singhal, G.; Jain, A.; Patnaik, A.: Parallelization of particle swarm optimization using message passing interfaces (MPIs). In: IEEE World Congress on Nature and Biologically Inspired Computing, pp. 67-71 (2009)Google Scholar
  38. 38.
    Lorion, Y.; Bogon, T.; Timm, I.J.; Drobnik, O.: An agent based parallel particle swarm optimization-APPSO. In: IEEE Swarm Intelligence Symposium, pp. 52–59 (2009)Google Scholar
  39. 39.
    Farmahini-Farahani, A.; Vakili, S.; Fakhraie, S.M.; Safari, S.; Lucas, C.: Parallel scalable hardware implementation of asynchronous discrete particle swarm optimization. Eng. Appl. Artif. Intell. 23(2), 177–187 (2010)Google Scholar
  40. 40.
    Li, B.; Wada, K.: Communication latency tolerant parallel algorithm for particle swarm optimization. Parallel Comput. 37(1), 1–10 (2011)Google Scholar
  41. 41.
    Aljarah, I.; Ludwig, S.A.: Parallel particle swarm optimization clustering algorithm based on MapReduce methodology. In: Fourth IEEE World Congress on Nature and Biologically Inspired Computing, pp. 104–111 (2012)Google Scholar
  42. 42.
    Parsopoulos, K.E.: Parallel cooperative micro-particle swarm optimization: a master slave model. Appl. Soft Comput. 12(11), 3552–3579 (2012)Google Scholar
  43. 43.
    Gulcu, S.; Kodaz, H.: A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization. Eng. Appl. Artif. Intell. 45, 33–45 (2015)Google Scholar
  44. 44.
    Zhang, G.W.; Zhan, Z.H.; Du, K.J.; Lin, Y.; Chen, W.N.; Li, J.J.; Zhang, J.: Parallel particle swarm optimization using message passing interface. In: Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, vol. 1, pp. 55–64 (2015)Google Scholar
  45. 45.
    Cao, J.; Cui, H.; Shi, H.; Jiao, L.: Big Data: a parallel particle swarm optimization-back-propagation neural network algorithm based on MapReduce. PLoS ONE 11(6), e0157551 (2016)Google Scholar
  46. 46.
    Tian, N.; Wang, Y.; Ji, Z.: Parallel coevolution of quantum-behaved particle swarm optimization for high-dimensional problems. In: Asian Simulation Conference, pp. 367–376 (2016)Google Scholar
  47. 47.
    Nedjah, N.; Rogerio, M.C.; Luiza, M.M.: A fine-grained parallel particle swarm optimization on many core and multi-core architectures. In: International Conference on Parallel Computing Technologies, pp. 215–224 (2017)Google Scholar
  48. 48.
    Arash, A.; Bernabe, D.; Gregoire, D.; Pascal, B.: A scalable parallel cooperative coevolutionary PSO algorithm for multi-objective optimization. J. Parallel Distrib. Comput. 112, 111–125 (2018)Google Scholar
  49. 49.
    Lai, X.; Zhou, Y.: An adaptive parallel particle swarm optimization for numerical optimization problems. Neural Comput. Appl. 1–19 (2018)Google Scholar
  50. 50.
    Li, Y.; Cao, Y.; Liu, Z.; Liu, Y.; Jiang, Q.: Dynamic optimal reactive power dispatch based on parallel particle swarm optimization algorithm. Comput. Math. Appl. 57(11), 1835–1842 (2009)zbMATHGoogle Scholar
  51. 51.
    Subbaraj, P.; Rengaraj, R.; Salivahanan, S.; Senthilkumar, T.R.: Parallel particle swarm optimization with modified stochastic acceleration factors for solving large scale economic dispatch problem. Int. J. Electr. Power Energy Syst. 32(9), 1014–1023 (2010)Google Scholar
  52. 52.
    Li, Z.; Chen, Y.: Design and implementation for parallel particle swarm optimization color quantization algorithm. In: IEEE International Conference on Computer and Information Application, pp. 339–342 (2010)Google Scholar
  53. 53.
    Prasain, H.; Jha, G.K.; Thulasiraman, P.; Thulasiram, R.: A parallel particle swarm optimization algorithm for option pricing. In: IEEE International Symposium on Parallel and Distributed Processing, Workshops and PhD Forum (IPDPSW), pp. 1–7 (2010)Google Scholar
  54. 54.
    Qi, J.; Guo, Q.; Lin, J.; Zhou, M.; Zhang, S.: Parallel particle swarm optimization algorithm of inverse heat conduction problem. In: Ninth IEEE International Symposium on Distributed Computing and Applications to Business Engineering and Science, pp. 5-9 (2010)Google Scholar
  55. 55.
    Drias, H.: Parallel swarm optimization for web information retrieval. In: Third IEEE World Congress on Nature and Biologically Inspired Computing, pp. 249–254 (2011)Google Scholar
  56. 56.
    Torres, S.P.; Castro, C.A.: Parallel particle swarm optimization applied to the static transmission expansion planning problem. In: Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition, pp. 1–6 (2012)Google Scholar
  57. 57.
    Omkar, S.N.; Venkatesh, A.; Mudigere, M.: MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures. Eng. Appl. Artif. Intell. 25(8), 1611–1627 (2012)Google Scholar
  58. 58.
    Wang, F.; Philip, L.H.; Cheung, D.W.: Combining technical trading rules using parallel particle swarm optimization based on Hadoop. In: IEEE International Joint Conference on Neural Networks, pp. 3987–3994 (2014)Google Scholar
  59. 59.
    Satapathy, A.; Satapathy, S.K.; Reza, M.: Agent-based parallel particle swarm optimization based on group collaboration. In: Annual IEEE India Conference, INDICON, pp. 1–5 (2014)Google Scholar
  60. 60.
    Xu, X.; Li, J.; Chen, H.l.: Enhanced support vector machine using parallel particle swarm optimization. In: 10th IEEE International Conference on Natural Computation, pp. 41–46 (2014)Google Scholar
  61. 61.
    Mohana, R.S.: A position balanced parallel particle swarm optimization method for resource allocation in cloud. Indian J. Sci. Technol. 8(S3), 182–188 (2015)Google Scholar
  62. 62.
    Chen, H.L.; Yang, B.; Wang, S.J.; Wang, G.; Li, H.Z.; Liu, W.B.: Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy. Appl. Math. Comput. 239, 180–197 (2014)MathSciNetzbMATHGoogle Scholar
  63. 63.
    Gou, J.; Wang, F.; Luo, W.: Mining fuzzy association rules based on parallel particle swarm optimization algorithm. Intell. Autom. Soft Comput. 2(2), 147–162 (2015)Google Scholar
  64. 64.
    Govindarajan, K.; Boulanger, D.; Kumar, V.S.: Parallel particle swarm optimization (PPSO) clustering for learning analytics. In: IEEE International Conference on Big Data, pp. 1461–1465 (2015)Google Scholar
  65. 65.
    Fukuyama, Y.: Parallel particle swarm optimization for reactive power and voltage control investigating dependability. In: 18th IEEE International Conference on Intelligent System Application to Power Systems, pp. 1–6 (2015)Google Scholar
  66. 66.
    Yuan, S.; Ji, F.; Yan, J.; Mu, B.: A parallel sensitive area selection-based particle swarm optimization algorithm for fast solving CNOP. In: International Conference on Neural Information Processing, pp. 71–78 (2015)Google Scholar
  67. 67.
    Kumar, P.R.; Babu, P.; Palani, S.: Particle swarm optimization based sequential and parallel tasks scheduling model for heterogeneous multiprocessor systems. Fundamenta Informaticae 139(1), 43–65 (2015)MathSciNetzbMATHGoogle Scholar
  68. 68.
    Moraes, A.O.S.; Mitre, J.F.; Lage, P.L.C.; Secchi, A.R.: A robust parallel algorithm of the particle swarm optimization method for large dimensional engineering problems. Appl. Math. Model. 39(14), 4223–4241 (2015)MathSciNetGoogle Scholar
  69. 69.
    Kusetogullari, H.; Yavariabdi, A.; Celik, T.: Unsupervised change detection in multitemporal multispectral satellite images using parallel particle swarm optimization. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(5), 2151–2164 (2015)Google Scholar
  70. 70.
    Jia, Y.; Chi, S.: Back-analysis of soil parameters of the Malutang II concrete face rockfill dam using parallel mutation particle swarm optimization. Comput. Geotech. 65, 87–96 (2015)Google Scholar
  71. 71.
    Fukuyama, Y.: Verification of dependability on parallel particle swarm optimization based voltage and reactive power control. IFAC-PapersOnLine 48(30), 167–172 (2015)Google Scholar
  72. 72.
    Ma, J.; Man, K.L.; Guan, S.; Ting, T.O.; Wong, P.W.H.: Parameter estimation of photovoltaic model via parallel particle swarm optimization algorithm. Int. J. Energy Res. 40(3), 343–352 (2016)Google Scholar
  73. 73.
    Hossain, M.S.; Moniruzzaman, M.; Muhammad, G.; Ghoneim, A.; Alamri, A.: Big data-driven service composition using parallel clustered particle swarm optimization in mobile environment. IEEE Trans. Serv. Comput. 9(5), 806–817 (2016)Google Scholar
  74. 74.
    Yuan, J.; Wang, L.; Xie, J.; Zhang, X.; Feng, E.; Yin, H.; Xiu, Z.: Modelling and parameter identification of a nonlinear enzyme-catalytic time-delayed switched system and its parallel optimization. Appl. Math. Model. 40(19), 8276–8295 (2016)MathSciNetGoogle Scholar
  75. 75.
    Ting, T.O.; Ma, J.; Kim, K.S.; Huang, K.: Multicores and GPU utilization in parallel swarm algorithm for parameter estimation of photovoltaic cell model. Appl. Soft Comput. 40, 58–63 (2016)Google Scholar
  76. 76.
    Sheng-li, L.; Ben-xi, L.; Chun-tian, C.; Zhi-fu, L.; Xin-yu, W.: Long-term generation scheduling of hydropower system using multi-core parallelization of particle swarm optimization. Water Resour. Manag. 31(9), 1–17 (2017)Google Scholar
  77. 77.
    Xin, L.; Wang, G.; Miao, S.; Li, X.: Optimal design of a hydraulic excavator working device based on parallel particle swarm optimization. J. Braz. Soc. Mech. Sci. Eng., pp. 1–13 (2017)Google Scholar
  78. 78.
    Luu, K.; Noble, M.; Gesret, A.; Belayouni, N.; Roux, P.-F.: A parallel competitive particle swarm optimization for non-linear first arrival traveltime tomography and uncertainty quantification. Comput. Geosci. 113, 81–93 (2018)Google Scholar
  79. 79.
    Nouiri, M.; Bekrar, A.; Jemai, A.; Niar, S.; Ammari, A.C.: An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J. Intell. Manuf. 29(3), 603–615 (2018)Google Scholar
  80. 80.
    Yoshida, H.; Fukuyama, Y.: Parallel multi-population differential evolutionary particle swarm optimization for voltage and reactive power control in electric power systems. In: 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp. 1240–1245 (2017)Google Scholar
  81. 81.
    Chu, S.C.; Pan, J.S.: Intelligent parallel particle swarm optimization algorithms. Parallel Evolut. Comput. 22, 159–175 (2006)Google Scholar
  82. 82.
    Waintraub, M.; Schirru, R.; Pereira, C.: Multiprocessor modeling of parallel particle swarm optimization applied to nuclear engineering problems. Prog. Nucl. Energy 51(6), 680–688 (2009)Google Scholar
  83. 83.
    Sivanandam, S.N.; Visalakshi, P.: Dynamic task scheduling with load balancing using parallel orthogonal particle swarm optimisation. Int. J. Bio-Inspir. Comput. 1(4), 276–286 (2009)Google Scholar
  84. 84.
    Tu, K.Y.; Liang, Z.C.: Parallel computation models of particle swarm optimization implemented by multiple threads. Expert Syst. Appl. 38(5), 5858–5866 (2011)Google Scholar
  85. 85.
    Zhang, Y.; Gallipoli, D.; Augarde, C.E.: Simulation based calibration of geotechnical parameters using parallel hybrid moving boundary particle swarm optimization. Comput. Geotech. 36(4), 604–615 (2009)Google Scholar
  86. 86.
    Roberge, V.; Tarbouchi, M.; Gilles, L.: Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Ind. Inform. 9(1), 132–141 (2013)Google Scholar
  87. 87.
    Jin, N.; Rahmat-Samii, Y.: Parallel particle swarm optimization and finite-difference time-domain (PSO/FDTD) algorithm for multiband and wide-band patch antenna designs. IEEE Trans. Antennas Propag. 53(11), 3459–3468 (2005)Google Scholar
  88. 88.
    Han, X.G.; Wang, F.; Fan, J.W.: The research of PID controller tuning based on parallel particle swarm optimization. Appl. Mech. Mater. 433, 583–586 (2013)Google Scholar
  89. 89.
    Chen, Y.Y.; Cheng, C.Y.; Wang, L.C.; Chen, T.L.: A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems: a case study for solar cell industry. Int. J. Prod. Econ., 141(1), 66–78 (2013)Google Scholar
  90. 90.
    Soares, J.; Vale, Z.; Canizes, B.; Morais, H.: Multi-objective parallel particle swarm optimization for day-ahead vehicle-to-grid scheduling. In: IEEE Symposium on Computational Intelligence Applications in Smart Grid, pp. 138–145 (2013)Google Scholar
  91. 91.
    Yuan, S.; Zhao, L.; Mu, B.: Parallel cooperative co-evolution based particle swarm optimization algorithm for solving conditional nonlinear optimal perturbation. In: International Conference on Neural Information Processing, pp. 87–95 (2015)Google Scholar
  92. 92.
    Cao, B.; Li, W.; Zhao, J.; Yang, S.; Kang, X.; Ling, Y.; Lv, Z.: Spark-based parallel cooperative co-evolution particle swarm optimization algorithm. In: IEEE International Conference on Web Services, pp. 570–577 (2016)Google Scholar
  93. 93.
    Long, H.X.; Li, M.Z.; Fu, H.Y.: Parallel quantum-behaved particle swarm optimization algorithm with neighborhood search. In: International Conference on Oriental Thinking and Fuzzy Logic, pp. 479–489 (2016)Google Scholar
  94. 94.
    Peng, Y.; Peng, A.; Zhang, X.; Zhou, H.; Zhang, L.; Wang, W.; Zhang, Z.: Multi-core parallel particle swarm optimization for the operation of inter-basin water transfer-supply systems. Water Resour. Manag. 31(1), 27–41 (2017)Google Scholar
  95. 95.
    Vlachogiannis, J.G.; Lee, K.Y.: Determining generator contributions to transmission system using parallel vector evaluated particle swarm optimization. IEEE Trans. Power Syst. 20(4), 1765–1774 (2005)Google Scholar
  96. 96.
    Fan, S.K.; Chang, J.M.: A parallel particle swarm optimization algorithm for multi-objective optimization problems. Eng. Optim. 41(7), 673–697 (2009)MathSciNetGoogle Scholar
  97. 97.
    Vlachogiannis, J.G.; Lee, K.Y.: Multi-objective based on parallel vector evaluated particle swarm optimization for optimal steady-state performance of power systems. Expert Syst. Appl. 36(8), 10802–10808 (2009)Google Scholar
  98. 98.
    Li, J-Z.; Chen, W-N.; Zhang, J.; Zhan, Z-H.: A parallel implementation of multiobjective particle swarm optimization algorithm based on decomposition. In: IEEE Symposium Series on Computational Intelligence, pp. 1310–1317 (2015)Google Scholar
  99. 99.
    Borges, N.; Soares, J.; Vale, Z.; Canizes, B.: Weighted sum approach using parallel particle swarm optimization to solve multi-objective energy scheduling. In: IEEE/PES Transmission and Distribution Conference and Exposition, pp. 1–5 (2016)Google Scholar
  100. 100.
    Li, J.; Wan, D.; Chi, Z.; Hu, X.: An efficient fine-grained parallel particle swarm optimization method based on GPU acceleration. Int. J. Innov. Comput. Inf. Control 3(6), 1707–1714 (2007)Google Scholar
  101. 101.
    Zhou, Y.; Tan, Y.: GPU based parallel particle swarm optimization. In: IEEE Congress on Evolutionary Computation, Trondheim, Norway, pp. 1493–1500 (2009)Google Scholar
  102. 102.
    Hung, Y.; Wang, W.: Accelerating parallel particle swarm optimization via GPU. Optim. Methods Softw. 27(1), 33–51 (2012)zbMATHGoogle Scholar
  103. 103.
    Zhu, H.; Guo, Y.; Wu, J.; Gu, J.; Eguchi, K.: Paralleling Euclidean particle swarm optimization in CUDA. In: 4th IEEE International Conference on Intelligent Networks and Intelligent Systems, pp. 93–96 (2011)Google Scholar
  104. 104.
    Kumar, J.; Singh, L.; Paul, S.: GPU based parallel cooperative particle swarm optimization using C-CUDA: a case study. In: IEEE International Conference on Fuzzy Systems, Hyderabad, India, pp. 1–8 (2013)Google Scholar
  105. 105.
    Calazan, R.M.; Nedjah, N.; Luiza, M.M.: Parallel GPU-based implementation of high dimension particle swarm optimizations. In IEEE Fourth Latin American Symposium on Circuits and Systems, pp. 1–4 (2013)Google Scholar
  106. 106.
    Shenghui, L.; Shuli, Z.: Research on FJSP based on CUDA parallel cellular particle swarm optimization algorithm. In: International IET Conference on Software Intelligence Technologies and Applications, pp. 325–329 (2014)Google Scholar
  107. 107.
    Li, J.; Wang, W.; Hu, X.: Parallel particle swarm optimization algorithm based on CUDA in the AWS cloud. In: Ninth International Conference on Frontier of Computer Science and Technology, pp. 8–12 (2015)Google Scholar
  108. 108.
    Hussain, M.; Hattori, H.; Fujimoto, N.: A CUDA implementation of the standard particle swarm optimization. In: 18th IEEE International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Romania, pp. 219–226 (2016)Google Scholar
  109. 109.
    Wachowiak, M.P.; Timson, M.C.; DuVal, D.J.: Adaptive particle swarm optimization with heterogeneous multicore parallelism and GPU acceleration. IEEE Trans. Parallel Distrib. Syst. 28(10), 2784–2793 (2017)Google Scholar
  110. 110.
    Chang, Y.L.; Fang, J.P.; Benediktsson, J.A.; Chang, L.; Ren, H.; Chen, K.S.: Band selection for hyperspectral images based on parallel particle swarm optimization schemes. IEEE Int. Geosci. Remote Sens. Symp. 5, 84–87 (2009)Google Scholar
  111. 111.
    Mussi, L.; Cagnoni, S.; Daolio, F.: GPU based road sign detection using particle swarm optimization. In: Ninth IEEE International Conference on Intelligent Systems Design and Applications, Pisa, Italy, pp. 152–157 (2009)Google Scholar
  112. 112.
    Liera, I.C.; Liera, M.A.C.; Castro, M.C.J.: Parallel particle swarm optimization using GPGPU. In: CIE (2011)Google Scholar
  113. 113.
    Roberge, V.; Tarbouchi, M.: Efficient parallel particle swarm optimizers on GPU for real-time harmonic minimization in multilevel inverters. In: 38th Annual Conference on IEEE Industrial Electronics Society, pp. 2275–2282 (2012)Google Scholar
  114. 114.
    Rabinovich, M.; Kainga, P.; Johnson, D.; Shafer, B.; Lee, J.J.; Eberhart, R.: Particle swarm optimization on a GPU. In: IEEE International Conference on Electro/Information Technology, pp. 1–6 (2012)Google Scholar
  115. 115.
    Datta, D.; Mehta, S.; Srivastava, R.: CUDA based particle swarm optimization for geophysical inversion. In: 1st IEEE International Conference on Recent Advances in Information Technology, Dhanbad, India, pp. 416–420 (2012)Google Scholar
  116. 116.
    Dali, N.; Bouamama, S.: GPU-PSO: parallel particle swarm optimization approaches on graphical processing unit for constraint reasoning: case of Max-CSPs. Procedia Comput. Sci. 60, 1070–1080 (2015)Google Scholar
  117. 117.
    Qu, J.; Liu, X.; Sun, M.; Qi, F.: GPU based parallel particle swarm optimization methods for graph drawing. Discrete Dyn. Nat. Soc., pp. 1–15 (2017)Google Scholar
  118. 118.
    Lorenzo, P.R.; Nalepa, J.; Ramos, L.S.; Pastor, J.R.: Hyper-parameter selection in deep neural networks using parallel particle swarm optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1864–1871 (2017)Google Scholar
  119. 119.
    Chih-Lun, L.; Shie-Jue, L.; Yu-Shu, C.; Ching-Ran, L.; Chie-Hong, L.: Power consumption minimization by distributive particle swarm optimization for luminance control and its parallel implementations. Expert Syst. Appl. 96, 479–491 (2018)Google Scholar
  120. 120.
    Laguna-Sanchez, G.A.; Mauricio, O.C.; Nareli, C.C.; Ricardo, B.F.; Cedillo, J.: Comparative study of parallel variants for a particle swarm optimization algorithm implemented on a multi-threading GPU. J. Appl. Res. Technol. 7(3), 292–307 (2009)Google Scholar
  121. 121.
    Mussi, L.; Daolio, F.; Cagnoni, S.: Evaluation of parallel particle swarm optimization algorithms within the CUDA: a architecture. Inf. Sci. 181(20), 4642–4657 (2011)Google Scholar
  122. 122.
    Altinoz, O.T.; Yilmaz, A.E.; Ciuprina, G.: Impact of problem dimension on the execution time of parallel particle swarm optimization implementation. In: 8th IEEE International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 1–6 (2013)Google Scholar
  123. 123.
    Nedjah, N.; Calazan, R.M.; Luiza, M.M.; Wang, C.: Parallel implementations of the cooperative particle swarm optimization on many-core and multi-core architectures. Int. J. Parallel Program. 44(6), 1173–1199 (2016)Google Scholar
  124. 124.
    Wu, Q.; Xiong, F.; Wang, F.; Xiong, Y.: Parallel particle swarm optimization on a graphics processing unit with application to trajectory optimization. Eng. Optim. 48(10), 1679–1692 (2016)MathSciNetGoogle Scholar
  125. 125.
    Franz, W.; Thulasiraman, P.: A dynamic cooperative hybrid MPSO+GA on hybrid CPU+GPU fused multicore. In: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8 (2016)Google Scholar
  126. 126.
    Ge, X.; Wang, H.; Fan, Y.; Cao, Y.; Chen, H.; Huang, R.: Joint inversion of T1–T2 spectrum combining the iterative truncated singular value decomposition and the parallel particle swarm optimization algorithms. Comput. Phys. Commun. 198, 59–70 (2016)Google Scholar
  127. 127.
    Jin, M.; Lu, H.: Parallel particle swarm optimization with genetic communication strategy and its implementation on GPU. In: IEEE 2nd International Conference on Cloud Computing and Intelligent Systems, vol. 1, pp. 99–104 (2012)Google Scholar
  128. 128.
    Zhou, Y.; Tan, Y.: GPU based parallel multi-objective particle swarm optimization. Int. J. Artif. Intell. 7(A11), 125–141 (2011)Google Scholar
  129. 129.
    Arun, J.P.; Mishra, M.; Subramaniam, S.V.: Parallel implementation of MOPSO on GPU using OpenCL and CUDA. In: 18th IEEE International Conference on High Performance Computing, pp. 1–10 (2011)Google Scholar
  130. 130.
    Zwokak, J.W.; Boggs, P.T.; Watson, L.T.: ODRPACK95, Technical Report. Masters thesis, Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA, (2004)Google Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringRajasthan Technical UniversityKotaIndia
  2. 2.School of Computer EngineeringKalinga Institute of Industrial TechnologyBhubaneswarIndia
  3. 3.Department of MathematicsIndian Institute of TechnologyRoorkeeIndia
  4. 4.South Asian UniversityNew DelhiIndia

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