基于多策略麝牛优化算法的稀布直线阵列综合

Sparse Linear Array Synthesis Based on Multi‑Strategy Musk Ox Optimizer

摘要: 面向6G线性稀布阵列综合的高维非线性寻优难题，传统智能优化算法易早熟收敛，旁瓣抑制效果有限。本文提出多策略麝牛优化算法(MSMO)用于阵元位置优化。该算法利用精英反向学习挖掘最优个体附近潜在优质解，借助停滞判定机制重置劣等个体维持种群多样性。采用自适应非线性Beta系数动态调整全局迁徙步长，结合适应度方差构建动态权重优化扰动幅度，并引入迭代衰减自适应莱维飞行强化局部搜索能力。实验结果证明所提算法拥有更优的收敛性能与寻优精度，可有效实现6G稀布线阵低旁瓣布阵设计。

Abstract: Aiming at the high-dimensional nonlinear optimization problem of linear sparse array synthesis for 6G, traditional intelligent optimization algorithms tend to suffer from premature convergence and achieve limited sidelobe suppression performance. In this paper, a multi‑strategy Musk Ox Optimizer (MSMO) is proposed for element position optimization. Elite opposition-based learning is adopted to explore potential high-quality solutions around the optimal individuals, and a stagnation judgment mechanism is utilized to reset inferior individuals so as to maintain population diversity. An adaptive nonlinear Beta coefficient is employed to dynamically adjust the global migration step size. Combined with fitness variance, dynamic weights are constructed to optimize the perturbation amplitude. Besides, iteratively decaying adaptive Lévy flight is introduced to strengthen local search capability. Experimental results verify that the proposed algorithm achieves better convergence performance and optimization accuracy, and can effectively realize the low-sidelobe layout design of 6G sparse linear arrays.