Comparison of nature-inspired techniques in design optimization of non-uniformly spaced arrays in the presence of mutual coupling

Abstract

In this paper, we investigate the performance of various nature-inspired optimization algorithms in the design and analysis of non-uniformly spaced arrays. These algorithms include covariance matrix adaptation evolution strategy (CMA-ES), evolutionary programming (EP), particle swarm optimization (PSO), genetic algorithm (GA), and fireworks algorithm (FWA). First, we compare the performance of these algorithms in optimal placements of elements in non-uniformly spaced linear arrays with uniform weights in order to achieve minimized sidelobe level for a given beam-direction as well as for having nulls at specified angles. We consider linear arrays with both isotropic and finite-length dipole elements, and both without and with mutual coupling effects. We also present the optimization of both spacings and weights in the design of sparse Dolph-Chebyshev arrays in the presence of mutual coupling effects. In addition, we perform the optimization for the case of a two-dimensional planar array. Finally, we apply a variation of the algorithms to perform joint direction-of-arrival and mutual coupling estimation using structured sparse arrays such as nested and minimum redundancy arrays.