APPLICATION OF GLOBAL OPTIMIZATION TO PARTICLE IDENTIFICATION USING LIGHT-SCATTERING

Numerical methods of solving the inverse light scattering problem for spheres are presented. The methods are based on two stochastic global optimization techniques: Deep's random search and the multilevel singl e-linkage clustering analysis due to Rinnooy Kan and Timmer. Computati onal examples show that the radius and the refractive index of spheres comparable with or larger than the wavelength of light can be recover ed from multiangle scattering data. While the random search approach i s faster, the clustering analysis is shown to be more reliable. A gene ral discussion of the clustering method is also given.