Parallel global optimization with the particle swarm algorithm

By Jaco F. Schutte, J. Reinbolt, Benjamin J. Fregly, Raphael T. Haftka, Alan D. George. International Journal for Numerical Methods in Engineering, 2004

411 citations recorded by the source corpus.

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Supervised learning of fuzzy ARTMAP neural networks through particle swarm optimization (2007, methodology): “For enhanced computational throughput and global search capabilities, Algorithm 2 is inspired by the synchronous parallel version of PSO [47].”
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, methodology): “[155] evaluated a parallel implementation of the algorithm on two types of test problems: a) large scale analytical problems with inexpensive function evaluations and b) medium scale problems on biomechanical system identification with computationally heavy function evaluations.”
A review of particle swarm optimization. Part I: background and development (2007, background): “Schutte et al. (2004) support these findings in their parallel solution to a largescale engineering optimization problem.”
Distributed evolutionary algorithms and their models: A survey of the state-of-the-art (2015, neutral): “In an experimental study of a master-slave PSO algorithm [110], it is shown that synchronization plays a vital role in algorithm performance on load-balanced problems, whilst asynchronous dEAs are more efficient for load-imbalanced problems.”