For the improvement of optical performance monitoring in transparent and reconfigurable optical networks using artificial neural networks trained with eye-diagram parameters, radial basis function artificial neural network models to simultaneously identify three separate impairments that can degrade optical channels, namely optical signal-to-noise ratio, chromatic dispersion, and polarization-mode dispersion, are developed. The neural networks are trained with the parameters derived from eye-diagram as inputs and the tested levels of concurrent impairment as outputs. They are optimized by hierarchical particle swarm optimization method. In the process of network optimization, the particle swarm inclines to small scales and small errors by choosing proper fitness functions. Finally, the prediction of levels of concurrent impairment drawn from the optimized models is realized by simulation experiments, and a better performance compared with those based on backward propagation artificial neural network models under the same testing circumstances is obtained.