Parallel Improvements of the Jaya Optimization Algorithm
Source abstract
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.
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Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Advanced Bandit Algorithms Research · Numerical Methods and Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Physics and Computer Architecture, Miguel Hernández University, Elche, E-03202 Alicante, Spain
Department of Computer Technology, University of Alicante, E-03071 Alicante, Spain
Funding
Ministerio de Economía y Competitividad · European Regional Development Fund
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