In this article, a classification framework that uses quantum-behaved particle swarm optimization neural network … function neural network (RBFNN). For training the RBFNN K-means clustering algorithm and quantum-behaved particle swarm … optimization (QPSO) algorithm has been used. The K-means clustering algorithm is used to find the optimal number of clusters which …

it. A hybrid particle swarm optimization algorithm HPSOD is proposed. First a PSO algorithm is designed, based on a sub …

Particle swarm optimization (PSO) is considered one of the most important methods in swarm intelligence. PSO is related …

Particle swarm optimization (PSO) is a swarm intelligence algorithm inspired by the social behavior of birds flocking … Inertia Weight PSO (IWPSO), the Constriction Factor PSO (CFPSO), and the Comprehensive Learning Particle Swarm Optimization …

conference paper Accelerating the Convergence of Adaptive Filters for Active Noise Control Using Particle Swarm Optimization … swarm optimization (PSO). Two objective functions were tested: one based on the response time itself and the other one based … coefficients, which are employed during the adaptive filter initialization and are obtained via a training process with particle …

falling into local optimum. This article proposes a hybrid particle swarm optimization (HPSO) technique that combines particle … proposed HPSO outperforms the simple particle swarm optimization in terms of fitness value, execution time, and error rate. … swarm optimization (PSO) and fruit fly (FOA) to perform the evolutionary search process. The following determines a pareto …