摘要
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。
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.The simulation results show that the method is efficient and stable,and has good popularization value.
作者
王停
夏克文
唐黎军
周伟
Wang Ting;Xia Kewen;Tang Lijun;Zhou Wei(School of Electronic Information Engineering,Hebei University of Technology,Tianjin 300000,China;People’s Liberation Army Air Force 93756,Tianjin 300401,China)
出处
《计算机应用研究》
CSCD
北大核心
2020年第8期2324-2328,共5页
Application Research of Computers
基金
国家自然科学基金资助项目
天津自然科学基金资助项目
河北省自然科学基金资助项目。
关键词
稀布阵列
约束优化问题
旁瓣电平
狼群算法
选择性变异
sparse array
constrained optimization problems
side-lobe level
wolf pack algorithm(WPA)
adaptive mutation
