Abstract
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.
Similar content being viewed by others
References
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. https://doi.org/10.1007/s00366-016-0484-8
Baños R, Ortega J, Gil C (2014) Comparing multicore implementations of evolutionary meta-heuristics for transportation problems. Ann Multicore GPU Progr 1(1):9–17
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 653–664
Blikberg R, Srevik T (2005) Load balancing and openMP implementation of nested parallelism. Parallel Comput 31(10):984–998. https://doi.org/10.1016/j.parco.2005.03.018
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. https://doi.org/10.1080/10426914.2017.1415441
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, Berlin
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. https://doi.org/10.1016/j.swevo.2011.02.002
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. https://doi.org/10.1016/j.advengsoft.2018.02.005
Free Software Foundation, Inc.: GCC, the GNU compiler collection. https://www.gnu.org/software/gcc/index.html
Gambhir M, Gupta S (2018) Advanced optimization algorithms for grating based sensors: a comparative analysis. Optik 164:567–574. https://doi.org/10.1016/j.ijleo.2018.03.062
Ghavidel S, Azizivahed A, Li L (2018) A hybrid Jaya algorithm for reliability-redundancy allocation problems. Eng Optim 50(4):698–715. https://doi.org/10.1080/0305215X.2017.1337755
Lin MH, Tsai JF, Yu CS (2012) A review of deterministic optimization methods in engineering and management. Math Probl Eng (Article ID 756023). https://doi.org/10.1155/2012/756023
Migallón H, Jimeno-Morenilla A, Sánchez-Romero JL (2018) Parallel improvements of the Jaya optimization algorithm. Appl Sci. https://doi.org/10.3390/app8050819
Mishra S, Ray PK (2016) Power quality improvement using photovoltaic fed DSTATCOM based on Jaya optimization. IEEE Trans Sustain Energy 7(4):1672–1680. https://doi.org/10.1109/TSTE.2016.2570256
MPI Forum: MPI: A Message-Passing Interface Standard. Version 2.2 (2009). Available at: http://www.mpi-forum.org
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 123:162–180
OpenMP Architecture Review Board: OpenMP Application Program Interface, version 3.1 (2011). http://www.openmp.org
Rao R, More K (2017) Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm. Energy Convers Manag 140:24–35. https://doi.org/10.1016/j.enconman.2017.02.068
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. https://doi.org/10.5267/j.ijiec.2015.8.004
Rao RV, Rai DP (2017) Optimisation of welding processes using quasi-oppositional-based Jaya algorithm. J Exp Theor Artif Intell 29(5):1099–1117. https://doi.org/10.1080/0952813X.2017.1309692
Rao RV, Rai DP, Balic J (2017) A multi-objective algorithm for optimization of modern machining processes. Eng Appl Artif Intell 61:103–125. https://doi.org/10.1016/j.engappai.2017.03.001
Rao RV, Saroj A (2017) A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm Evol Comput 37:1–26. https://doi.org/10.1016/j.swevo.2017.04.008
Rao RV, Saroj A (2017) Constrained economic optimization of shell-and-tube heat exchangers using elitist-Jaya algorithm. Energy 128:785–800. https://doi.org/10.1016/j.energy.2017.04.059
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. https://doi.org/10.1016/j.cad.2010.12.015
Rao RV, Waghmare G (2017) A new optimization algorithm for solving complex constrained design optimization problems. Eng Optim 49(1):60–83. https://doi.org/10.1080/0305215X.2016.1164855
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. https://doi.org/10.1016/j.engappai.2017.01.008
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. https://doi.org/10.5815/ijieeb.2015.01.08
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. https://doi.org/10.5815/ijisa.2015.07.08
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. https://doi.org/10.1016/j.neucom.2017.08.015
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. https://doi.org/10.1016/j.enconman.2017.08.063
Acknowledgements
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).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Migallón, H., Jimeno-Morenilla, A., Sánchez-Romero, J.L. et al. Multipopulation-based multi-level parallel enhanced Jaya algorithms. J Supercomput 75, 1697–1716 (2019). https://doi.org/10.1007/s11227-019-02759-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-019-02759-z