Synthesis of linear aperiodic arrays using a self-adaptive hybrid differential evolution algorithm

A self-adaptive hybrid differential evolution algorithm is proposed to optimise linear aperiodic arrays with a minimum peak sidelobe level. The proposed method incorporates a local search operation into a differential evolution (DE) algorithm to accelerate its convergence rate. Moreover, the self-adaptive parameter control approach is employed to avoid tuning the parameters of the DE algorithm. Synthesis examples are compared with the linear aperiodic array designs in the literature. Numerical results demonstrate that our approach is superior to existing algorithms in terms of the high-quality solutions and relatively small computational cost.