改进狼群算法用于多约束稀布线阵综合

结合量子理论提出了一种改进狼群算法，并将其用于优化多约束稀布直线阵列综合问题。新算法通过量子位特殊编码方式、停滞检测与选择性变异极大地提高了全局优化能力。给出了改进狼群算法流程，并在给定阵列孔径和阵元数的条件下，实现了任意最小阵元间距约束下，抑制天线峰值旁瓣电平（PSLL）的稀布线阵综合仿真。通过解空间变换，有效避免了算法进行阵列综合时，狼群位置更新过程中出现不可行解问题，减少了判断步骤，提高了优化效率。通过典型实例的仿真对比，证实了该方法的有效性和稳健性，而且能获得比现有方法更低的PSLL和更高的优化效率。

Improved wolf pack algorithm for multi-constrained sparse array Synthesis

Based on quantum theory, this paper proposed an improved wolf pack optimization algorithm for the comprehensive optimization of sparse linear arrays with multiple constraints. The new algorithm greatly improved the global optimization ability through the special coding of quantum bits, stagnation detection and selective mutation. It gave the flow chart of the improved wolf swarm algorithm. Given the array aperture and the number of elements, the algorithm could simulate the peak side-lobe level(PSLL) of sparse linear arrays with arbitrary minimum array spacing constraints. By solving the space transformation, it effectively avoided the infeasible solution in the process of updating wolf group position, reduced the judgment steps, and improved the optimization efficiency is improved. The simulation results show that the method is efficient and stable, and has good popularization value.