Multipopulation-based multi-level parallel enhanced Jaya algorithms
- 68 Downloads
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.
KeywordsJaya 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.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
- 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.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
- 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
- 9.Free Software Foundation, Inc.: GCC, the GNU compiler collection. https://www.gnu.org/software/gcc/index.html
- 12.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
- 13.Migallón H, Jimeno-Morenilla A, Sánchez-Romero JL (2018) Parallel improvements of the Jaya optimization algorithm. Appl Sci. https://doi.org/10.3390/app8050819
- 15.MPI Forum: MPI: A Message-Passing Interface Standard. Version 2.2 (2009). Available at: http://www.mpi-forum.org
- 17.OpenMP Architecture Review Board: OpenMP Application Program Interface, version 3.1 (2011). http://www.openmp.org