Parallel Improvements of the Jaya Optimization Algorithm

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...

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Published in:	Applied sciences Vol. 8; no. 5; p. 819
Main Authors:	Migallón, Héctor, Jimeno-Morenilla, Antonio, Sanchez-Romero, Jose-Luis
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 18.05.2018
Subjects:	Data mining Design optimization Genetic algorithms Heuristic Linear algebra Methods Optimization algorithms Population Software Germany Spain
ISSN:	2076-3417, 2076-3417
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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.
AbstractList	An field of intense work of the scientific community is artificial intelligence [26], in which neural-symbolic computation [27] is a key challenge, especially to construct computational cognitive models that admit integrated algorithms for learning and reasoning that can be treated computationally. [...]deep learning is not an optimization algorithm in itself, but the deep network has an objective function, so a heuristic optimization algorithm can be used to tune the network. [...]in [15], the authors explore the use of advanced optimization algorithms for determining optimum parameters for grating based sensors; in particular, Cuckoo search, PSO, TLBO, and Jaya algorithms were evaluated. [...]following parallel computation, the “sequential thread (or process)” obtains the best global solution and computes statistical values of all solutions obtained. [...]the conclusions obtained, through the comparison performed in [3] with respect to other well known optimization heuristic techniques, can be applied to the parallel proposals analyzed. 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.
Author	Sanchez-Romero, Jose-Luis Jimeno-Morenilla, Antonio Migallón, Héctor
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Title	Parallel Improvements of the Jaya Optimization Algorithm
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