Next Article in Journal
Heat Transfer Designed for Bionic Surfaces with Rib Turbulators Inspired by Alopias Branchial Arch in a Simplified Gas Turbine Transition Piece
Previous Article in Journal
Effect of Carrier Localization on Recombination Processes and Efficiency of InGaN-Based LEDs Operating in the “Green Gap”
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(5), 819; https://doi.org/10.3390/app8050819

Parallel Improvements of the Jaya Optimization Algorithm

1
Department of Physics and Computer Architecture, Miguel Hernández University, Elche, E-03202 Alicante, Spain
2
Department of Computer Technology, University of Alicante, E-03071 Alicante, Spain
*
Author to whom correspondence should be addressed.
Received: 9 May 2018 / Revised: 16 May 2018 / Accepted: 16 May 2018 / Published: 18 May 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
View Full-Text   |   Download PDF [824 KB, uploaded 21 May 2018]   |  

Abstract

A wide range of applications use optimization algorithms to find an optimal value, often a minimum one, for a given function. Depending on the application, both the optimization algorithm’s behavior, and its computational time, can prove to be critical issues. In this paper, we present our efficient parallel proposals of the Jaya algorithm, a recent optimization algorithm that enables one to solve constrained and unconstrained optimization problems. We tested parallel Jaya algorithms for shared, distributed, and heterogeneous memory platforms, obtaining good parallel performance while leaving Jaya algorithm behavior unchanged. Parallel performance was analyzed using 30 unconstrained functions reaching a speed-up of up to 57.6 x using 60 processors. For all tested functions, the parallel distributed memory algorithm obtained parallel efficiencies that were nearly ideal, and combining it with the shared memory algorithm allowed us to obtain good parallel performance. The experimental results show a good parallel performance regardless of the nature of the function to be optimized. View Full-Text
Keywords: Jaya; optimization problems; parallel; heuristic; OpenMP; MPI; hybrid MPI/OpenMP Jaya; optimization problems; parallel; heuristic; OpenMP; MPI; hybrid MPI/OpenMP
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Migallón, H.; Jimeno-Morenilla, A.; Sanchez-Romero, J.-L. Parallel Improvements of the Jaya Optimization Algorithm. Appl. Sci. 2018, 8, 819.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top