Constrained Sparse Array Pattern Synthesis Based on Quantum Lion Swarm Algorithm

Sparse array pattern synthesis constitutes a critical challenge in array antenna design. Traditional intelligent optimization methods often suffer from slow convergence speed and proneness to falling into local optima. To address these issues, this paper proposes a constrained sparse array pattern synthesis method based on the Quantum Lion Swarm Optimization (QLSO). By updating the positions of the lion swarm through quantum rotation gate operations and quantum rotation angle adjustments, the method achieves a balanced trade-off between global exploration of the solution space and local exploitation of promising regions. Specifically, a penalty-mechanism-based fitness function is designed to evaluate each candidate array, this function integrates key constraint conditions and core optimization objectives, and imposes penalties on constraint-violating solutions to guide the effective evolutionary search of the population.Simulation experiments are conducted to compare QLSO with classical algorithms in the context of constrained sparse array pattern synthesis. The results demonstrate that while satisfying the beamwidth and sparsity constraints, QLSO exhibits faster convergence speed, stronger global optimization capability, and superior sidelobe suppression performance. The proposed QLSO-based method provides an efficient and reliable approach for constrained sparse array pattern synthesis.