Sparse Array Design Based on the Combination of Improved Binary Grey Wolf Optimisation and Genetic Algorithm

Abstract

Traditional adaptive beamforming techniques focus solely on optimising the excitation weights of array elements while neglecting the critical influence of element positioning on beamforming performance. To enhance array degrees of freedom and achieve superior beamforming capabilities, this paper proposes a novel joint optimisation method that simultaneously adjusts both element positions and excitation coefficients, targeting maximum output signal-to-interference-plus-noise ratio (MaxSINR). Under the minimum variance distortionless response (MVDR) framework, we derive and analyse the theoretical relationship between output SINR and array configuration. We reformulate the sparse array design as a binary integer optimisation problem by introducing a position selection vector. The solution is efficiently obtained through our enhanced hybrid algorithm, which combines improved binary grey wolf optimisation with genetic algorithm (IBGWO-GA). Compared with the traditional beamforming method, the proposed algorithm can effectively improve the degree of freedom of the array position and realise interference suppression under underdetermined conditions. The optimal design of sparse linear array and sparse planar array in simulation experiments verifies the effectiveness of the proposed method.