Sparse Antenna Array Synthesis Method for Higher Efficiency and Lower Cost via Adaptive Multipoint Mutation Genetic Algorithm

Abstract

Large-scale uniform arrays encounter critical challenges due to dense element arrangements, including excessive hardware, elevated computational complexity, high costs, strong mutual coupling, and suboptimal sidelobe suppression. To address these issues, sparse array design offers notable advantages. In this study, we innovatively propose a sparse synthesis approach for array sparsification. The method employs an adaptive multipoint mutation genetic algorithm (AMPMGA) to optimize element layout, targeting both peak SLL (PSLL) reduction and radiation gain enhancement. The incorporation of an equidistant sampling cross-strategy in AMPMGA enhances operational efficiency and a multipoint mutation strategy to avoid getting stuck in a local optimal solution, which also accelerates the convergence speed of the algorithm. Compared to existing methods, our approach demonstrates faster convergence, stronger global search capability, and robust beam-sweeping characteristics that are unconstrained by update speed or trajectory limitations. The verification of AMPMGA effectiveness was also conducted through full-wave simulation experiments. The optimized sparse arrays achieved an efficient balance among technical performance, cost, and system complexity. This method delivers practical value to array systems and offers an innovative solution for array sparsification design.