The particle swarm optimization algorithm with prior low precision, divergent character and slow late convergence is improved by joining the random variable satisfied the Gaussian distribution to individual extremum, namely searching locally the individual extremum by Gaussian operator.
A number of recently proposed variants of the particle swarm optimization algorithm (PSOA) are applied to an extended Dixon-Szeg und constrained test set in global optimization.
We propose a new Memetic Particle Swarm Optimization scheme that incorporates local search techniques in the standard Particle Swarm Optimization algorithm, resulting in an efficient and effective optimization method, which is analyzed theoretically.
The technique combines a particle swarm optimization algorithm with a neural network to create features that robustly detect and track HFEOs in human intracranial EEG (IEEG) recordings.
A Particle Swarm Optimization Algorithm with Path Relinking for the Location Routing Problem
Hidden Markov Models Training by a Particle Swarm Optimization Algorithm