The particle size distribution (PSD) plays an important role in environmental
pollution detection and human health protection, such as fog, haze and soot.
In this study, the Attractive and Repulsive Particle Swarm Optimization
(ARPSO) algorithm and the basic PSO were applied to retrieve the PSD. The
spectral extinction technique coupled with the Anomalous Diffraction
Approximation (ADA) and the Lambert-Beer Law were employed to investigate
the retrieval of the PSD. Three commonly used monomodal PSDs, i.e. the
Rosin-Rammer (R-R) distribution, the normal (N-N) distribution, the
logarithmic normal (L-N) distribution were studied in the dependent model.
Then, an optimal wavelengths selection algorithm was proposed. To study the
accuracy and robustness of the inverse results, some characteristic
parameters were employed. The research revealed that the ARPSO showed more
accurate and faster convergence rate than the basic PSO, even with random
measurement error. Moreover, the investigation also demonstrated that the
inverse results of four incident laser wavelengths showed more accurate and
robust than those of two wavelengths. The research also found that if
increasing the interval of the selected incident laser wavelengths, inverse
results would show more accurate, even in the presence of random error.
Hardware in the Loop Simulation of Control Optimal of DC Motor Base on Modified Quantum-Behaved Particle Swarm Optimization