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Electronics 2019, 8(1), 19; https://doi.org/10.3390/electronics8010019

Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

1
Department of Physics and Computer Architecture, Miguel Hernández University, E-03202 Alicante, Spain
2
Department of Computer Technology, University of Alicante, E-03071 Alicante, Spain
3
Sardar Vallabhbhai National Institute of Technology, Surat 395 007, Gujarat State, India
*
Author to whom correspondence should be addressed.
Received: 27 November 2018 / Revised: 11 December 2018 / Accepted: 21 December 2018 / Published: 23 December 2018
(This article belongs to the Section Computer Science & Engineering)
Full-Text   |   PDF [303 KB, uploaded 26 December 2018]

Abstract

A numerous group of optimization algorithms based on heuristic techniques have been proposed in recent years. Most of them are based on phenomena in nature and require the correct tuning of some parameters, which are specific to the algorithm. Heuristic algorithms allow problems to be solved more quickly than deterministic methods. The computational time required to obtain the optimum (or near optimum) value of a cost function is a critical aspect of scientific applications in countless fields of knowledge. Therefore, we proposed efficient algorithms parallel to Teaching-learning-based optimization algorithms. TLBO is efficient and free from specific parameters to be tuned. The parallel proposals were designed with two levels of parallelization, one for shared memory platforms and the other for distributed memory platforms, obtaining good parallel performance in both types of parallel architectures and on heterogeneous memory parallel platforms. View Full-Text
Keywords: TLBO; optimization problems; parallel; heuristic; subpopulations; OpenMP; MPI; hybrid MPI/OpenMP TLBO; optimization problems; parallel; heuristic; subpopulations; OpenMP; MPI; hybrid MPI/OpenMP
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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García-Monzó, A.; Migallón, H.; Jimeno-Morenilla, A.; Sánchez-Romero, J.-L.; Rico, H.; Rao, R.V. Efficient Subpopulation Based Parallel TLBO Optimization Algorithms. Electronics 2019, 8, 19.

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