RT Journal Article
T1 Parallelizing Particle Swarm Optimization in a Functional Programming Environment
A1 Rabanal Basalo, Pablo Manuel
A1 Rodríguez Laguna, Ismael
A1 Rubio Díez, Fernando
AB 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.
PB MDPI
SN 1999-4893
YR 2014
FD 2014-10-23
LK https://hdl.handle.net/20.500.14352/34381
UL https://hdl.handle.net/20.500.14352/34381
LA eng
NO Ministerio de Ciencia e Innovación (MICINN)
DS Docta Complutense
RD 6 abr 2025