Multipopulation-based multi-level parallel enhanced Jaya algorithms


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.

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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).

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Correspondence to H. Migallón.

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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).

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  • Jaya
  • Optimization
  • Metaheuristic
  • Multipopulation
  • Parallelism
  • MPI/OpenMP