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In this paper, a diversity guided particle swarm optimization (DGPSO-BP) guided by diversity and fitness value is firstly proposed to address two problems: premature convergence in the standard PSO and longer searching time brought by the optimization of the PSO. Further, the DGPSO-BP is combined with back-propagation (BP) for feed forward neural networks to avoid the problem of being trapped into...
In this paper, an improved particle swarm optimization (PSO) with the improved diversity is proposed to train feedforward neural networks (FNN). In this algorithm, first, the PSO algorithm is used to train the FNN. Second, when the particle swarm is trapped into local minima or loses its diversity, each particle in the swarm and its best position (Pb) are interrupted by a random function in order...
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