The Journal of Supercomputing

, Volume 75, Issue 3, pp 1094–1106 | Cite as

Jaya optimization algorithm with GPU acceleration

  • A. Jimeno-Morenilla
  • J. L. Sánchez-Romero
  • H. MigallónEmail author
  • H. Mora-Mora


Optimization methods allow looking for an optimal value given a specific function within a constrained or unconstrained domain. These methods are useful for a wide range of scientific and engineering applications. Recently, a new optimization method called Jaya has generated growing interest because of its simplicity and efficiency. In this paper, we present the Jaya GPU-based parallel algorithms we developed and analyze both parallel performance and optimization performance using a well-known benchmark of unconstrained functions. Results indicate that parallel Jaya implementation achieves significant speed-up for all benchmark functions, obtaining speed-ups of up to \(190\times \), without affecting optimization performance.


Jaya Optimization Parallelism GPU CUDA 


  1. 1.
    Lin MH, Tsai JF, Yu CS (2012) A review of deterministic optimization methods in engineering and management. Math Prob Eng 2012:1–15.
  2. 2.
    Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, OxfordzbMATHGoogle Scholar
  3. 3.
    Rao RV, Patel V (2012) An elitist teaching–learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput 3:535–560Google Scholar
  4. 4.
    Rao RV, Patel V (2013) Comparative performance of an elitist teaching–learning-based optimization algorithm for solving unconstrained optimization problems. Int J Ind Eng Comput 4:29–50Google Scholar
  5. 5.
    Rao RV, Savsani V, Vakharia D (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315CrossRefGoogle Scholar
  6. 6.
    Rao RV, Rai DP, Balic J (2017) A multi-objective algorithm for optimization of modern machining processes. Eng Appl Artif Intell 61(Supplement C):103–125. (online)
  7. 7.
    Rao RV (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19–34Google Scholar
  8. 8.
    Singh SP, Prakash T, Singh V, Babu MG (2017) Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm. Eng Appl Artif Intell 60:35–44CrossRefGoogle Scholar
  9. 9.
    Gao K, Zhang Y, Sadollah A, Su R (2016) Jaya algorithm for solving urban traffic signal control problem. In: 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, pp 1–6Google Scholar
  10. 10.
    Azizipanah-Abarghooee R, Malekpour M, Zare M, Terzija V (2016) A new inertia emulator and fuzzy-based LFC to support inertial and governor responses using Jaya algorithm. In: Power and Energy Society General Meeting (PESGM). IEEE 2016, pp 1–5Google Scholar
  11. 11.
    Bhoye M, Pandya M, Valvi S, Trivedi IN, Jangir P, Parmar SA (2016) An emission constraint economic load dispatch problem solution with microgrid using Jaya algorithm. In: 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS). IEEE, pp 497–502Google Scholar
  12. 12.
    Trivedi IN, Purohit SN, Jangir P, Bhoye MT (2016) Environment dispatch of distributed energy resources in a microgrid using Jaya algorithm. In: 2016 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB). IEEE, pp 224–228Google Scholar
  13. 13.
    Umbarkar AJ, Joshi MS, Sheth PD (2015) Openmp dual population genetic algorithm for solving constrained optimization problems. Int J Inf Eng Electron Bus 1:59–65Google Scholar
  14. 14.
    Baños R, Ortega J, Gil C (2014) Comparing multicore implementations of evolutionary meta-heuristics for transportation problems. Ann Multicore GPU Program 1(1):9–17Google Scholar
  15. 15.
    Delisle P, Krajecki M, Gravel M, Gagné C (2001) Parallel implementation of an ant colony optimization metaheuristic with OpenMP. In: Proceedings of the 3rd European Workshop on OpenMP. Springer, BerlinGoogle Scholar
  16. 16.
    Tan Y, Ding K (2016) A survey on GPU-based implementation of swarm intelligence algorithms. IEEE Trans Cybern 46(9):2028–2041CrossRefGoogle Scholar
  17. 17.
    Luo GH, Huang SK, Chang YS, Yuan SM (2014) A parallel Bees Algorithm implementation on GPU. J Syst Arch 60(3):271–279. Real-time embedded software for multi-core platforms. (online)
  18. 18.
    Delvacq A, Delisle P, Gravel M, Krajecki M (2013) Parallel ant colony optimization on graphics processing units. J Parallel Distrib Comput 73(1):52–61. Metaheuristics on GPUs. (online)
  19. 19.
    Mussi L, Daolio F, Cagnoni S (2011) Evaluation of parallel particle swarm optimization algorithms within the CUDA architecture. Inf Sci 181(20):4642–4657. Special issue on interpretable fuzzy systems. (online)
  20. 20.
    Veronese LP, Krohling RA (2010) Differential evolution algorithm on the GPU with C-CUDA. In: IEEE Congress on Evolutionary Computation, July 2010, pp 1–7Google Scholar
  21. 21.
    Zhou Y, Tan Y (2009) GPU-based parallel particle swarm optimization. In: 2009 IEEE Congress on Evolutionary Computation, May 2009, pp 1493–1500Google Scholar
  22. 22.
    Rao RV, Waghmare G (2017) A new optimization algorithm for solving complex constrained design optimization problems. Eng Optim 49(1):60–83CrossRefGoogle Scholar
  23. 23.
    Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132. (online)

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

Authors and Affiliations

  1. 1.Department of Computer TechnologyUniversity of AlicanteAlicanteSpain
  2. 2.Department of Physics and Computer ArchitectureMiguel Hernández UniversityElcheSpain

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