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Algorithms 2014, 7(4), 554-581; doi:10.3390/a7040554

Parallelizing Particle Swarm Optimization in a Functional Programming Environment

Computer Science Faculty, Complutense University, Madrid 28040, Spain
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Received: 4 July 2014 / Revised: 4 October 2014 / Accepted: 14 October 2014 / Published: 23 October 2014
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Abstract

Many bioinspired methods are based on using several simple entities which search for a reasonable solution (somehow) independently. This is the case of Particle Swarm Optimization (PSO), where many simple particles search for the optimum solution by using both their local information and the information of the best solution found so far by any of the other particles. Particles are partially independent, and we can take advantage of this fact to parallelize PSO programs. Unfortunately, providing good parallel implementations for each specific PSO program can be tricky and time-consuming for the programmer. In this paper we introduce several parallel functional skeletons which, given a sequential PSO implementation, automatically provide the corresponding parallel implementations of it. We use these skeletons and report some experimental results. We observe that, despite the low effort required by programmers to use these skeletons, empirical results show that skeletons reach reasonable speedups. View Full-Text
Keywords: Particle Swarm Optimization; parallel programming; skeletons; functional programming Particle Swarm Optimization; parallel programming; skeletons; functional programming
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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MDPI and ACS Style

Rabanal, P.; Rodríguez, I.; Rubio, F. Parallelizing Particle Swarm Optimization in a Functional Programming Environment. Algorithms 2014, 7, 554-581.

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