The Journal of Supercomputing

, Volume 75, Issue 3, pp 1697–1716 | Cite as

Multipopulation-based multi-level parallel enhanced Jaya algorithms

  • H. MigallónEmail author
  • A. Jimeno-Morenilla
  • J. L. Sánchez-Romero
  • H. Rico
  • R. V. Rao


To solve optimization problems, in the field of engineering optimization, an optimal value of a specific function must be found, in a limited time, within a constrained or unconstrained domain. Metaheuristic methods are useful for a wide range of scientific and engineering applications, which accelerate being able to achieve optimal or near-optimal solutions. The metaheuristic method called Jaya has generated growing interest because of its simplicity and efficiency. We present Jaya-based parallel algorithms to efficiently exploit cluster computing platforms (heterogeneous memory platforms). We propose a multi-level parallel algorithm, in which, to exploit distributed-memory architectures (or multiprocessors), the outermost layer of the Jaya algorithm is parallelized. Moreover, in internal layers, we exploit shared-memory architectures (or multicores) by adding two more levels of parallelization. This two-level internal parallel algorithm is based on both a multipopulation structure and an improved heuristic search path relative to the search path of the sequential algorithm. The multi-level parallel algorithm obtains average efficiency values of 84% using up to 120 and 135 processes, and slightly accelerates the convergence with respect to the sequential Jaya algorithm.


Jaya Optimization Metaheuristic Multipopulation Parallelism MPI/OpenMP 



This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-66972-C5-4-R and Grant TIN2017-89266-R, co-financed by FEDER funds (MINECO/FEDER/UE).


  1. 1.
    Abhishek K, Kumar VR, Datta S, Mahapatra, SS (2016) Application of JAYA algorithm for the optimization of machining performance characteristics during the turning of CFRP (epoxy) composites: comparison with TLBO, GA, and ICA. Eng Comput.
  2. 2.
    Baños R, Ortega J, Gil C (2014) Comparing multicore implementations of evolutionary meta-heuristics for transportation problems. Ann Multicore GPU Progr 1(1):9–17Google Scholar
  3. 3.
    Baños R, Ortega J, Gil C (2014) Hybrid mpi/openmp parallel evolutionary algorithms for vehicle routing problems. In: Esparcia-Alcázar AI, Mora AM (eds) Applications of Evolutionary Computation: 17th European Conference, EvoApplications 2014, Granada, Spain, April 23–25, 2014, Revised Selected Papers. Springer, Berlin, pp 653–664Google Scholar
  4. 4.
    Blikberg R, Srevik T (2005) Load balancing and openMP implementation of nested parallelism. Parallel Comput 31(10):984–998. CrossRefGoogle Scholar
  5. 5.
    Choudhary A, Kumar M, Unune DR (2018) Investigating effects of resistance wire heating on AISI 1023 weldment characteristics during ASAW. Mater Manuf Process 33(7):759–769. CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Derrac J, Garca S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18. CrossRefGoogle Scholar
  8. 8.
    Dinh-Cong D, Dang-Trung H, Nguyen-Thoi T (2018) An efficient approach for optimal sensor placement and damage identification in laminated composite structures. Adv Eng Softw 119:48–59. CrossRefGoogle Scholar
  9. 9.
    Free Software Foundation, Inc.: GCC, the GNU compiler collection.
  10. 10.
    Gambhir M, Gupta S (2018) Advanced optimization algorithms for grating based sensors: a comparative analysis. Optik 164:567–574. CrossRefGoogle Scholar
  11. 11.
    Ghavidel S, Azizivahed A, Li L (2018) A hybrid Jaya algorithm for reliability-redundancy allocation problems. Eng Optim 50(4):698–715. MathSciNetCrossRefGoogle Scholar
  12. 12.
    Lin MH, Tsai JF, Yu CS (2012) A review of deterministic optimization methods in engineering and management. Math Probl Eng (Article ID 756023).
  13. 13.
    Migallón H, Jimeno-Morenilla A, Sánchez-Romero JL (2018) Parallel improvements of the Jaya optimization algorithm. Appl Sci.
  14. 14.
    Mishra S, Ray PK (2016) Power quality improvement using photovoltaic fed DSTATCOM based on Jaya optimization. IEEE Trans Sustain Energy 7(4):1672–1680. CrossRefGoogle Scholar
  15. 15.
    MPI Forum: MPI: A Message-Passing Interface Standard. Version 2.2 (2009). Available at:
  16. 16.
    Ocloń P, Cisek P, Rerak M, Taler D, Rao RV, Vallati A, Pilarczyk M (2018) Thermal performance optimization of the underground power cable system by using a modified Jaya algorithm. Int J Therm Sci 123:162–180CrossRefGoogle Scholar
  17. 17.
    OpenMP Architecture Review Board: OpenMP Application Program Interface, version 3.1 (2011).
  18. 18.
    Rao R, More K (2017) Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm. Energy Convers Manag 140:24–35. CrossRefGoogle Scholar
  19. 19.
    Rao RV (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19–34. Google Scholar
  20. 20.
    Rao RV, Rai DP (2017) Optimisation of welding processes using quasi-oppositional-based Jaya algorithm. J Exp Theor Artif Intell 29(5):1099–1117. CrossRefGoogle Scholar
  21. 21.
    Rao RV, Rai DP, Balic J (2017) A multi-objective algorithm for optimization of modern machining processes. Eng Appl Artif Intell 61:103–125. CrossRefGoogle Scholar
  22. 22.
    Rao RV, Saroj A (2017) A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm Evol Comput 37:1–26. CrossRefGoogle Scholar
  23. 23.
    Rao RV, Saroj A (2017) Constrained economic optimization of shell-and-tube heat exchangers using elitist-Jaya algorithm. Energy 128:785–800. CrossRefGoogle Scholar
  24. 24.
    Rao RV, Savsani V, Vakharia D (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput-Aid Des 43(3):303–315. CrossRefGoogle Scholar
  25. 25.
    Rao RV, Waghmare G (2017) A new optimization algorithm for solving complex constrained design optimization problems. Eng Optim 49(1):60–83. CrossRefGoogle Scholar
  26. 26.
    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 60:35–44. CrossRefGoogle Scholar
  27. 27.
    Umbarkar AJ, Joshi MS, Sheth PD (2015) Openmp dual population genetic algorithm for solving constrained optimization problems. Int J Inf Eng Electron Bus 1:59–65. Google Scholar
  28. 28.
    Umbarkar AJ, Rothe NM, Sathe A (2015) OpenMP teaching-learning based optimization algorithm over multi-core system. Int J Intell Syst Appl 7:19–34. Google Scholar
  29. 29.
    Wang SH, Phillips P, Dong ZC, Zhang YD (2018) Intelligent facial emotion recognition based on stationary wavelet entropy and Jaya algorithm. Neurocomputing 272:668–676. CrossRefGoogle Scholar
  30. 30.
    Yu K, Liang J, Qu B, Chen X, Wang H (2017) Parameters identification of photovoltaic models using an improved Jaya optimization algorithm. Energy Convers Manag 150:742–753. CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer EngineeringMiguel Hernández UniversityElcheSpain
  2. 2.Department of Computer TechnologyUniversity of AlicanteAlicanteSpain
  3. 3.Sardar Vallabhbhai National Institute of TechnologySuratIndia

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