基于混合三角变异差分进化算法的平面稀疏阵列约束优化

Planar Sparse Array Constraint Optimization Based on Hybrid Trigonometric Mutation Differential Evolution Algorithm

针对旁瓣零陷凹面约束的稀疏平面阵列优化及算法早熟等问题,该文基于参数自适应的思想,提出一种混合三角变异差分进化算法。通过引入旁瓣零陷凹面约束矩阵,构建自适应惩罚函数,时变权重组合变异策略与交叉策略,提高算法前期全局搜索能力和后期收敛能力,最终实现峰值旁瓣电平和旁瓣零陷凹面的平面阵列约束优化。仿真结果表明,对比混合三角变异策略前的算法,该算法在完成稀疏阵列峰值旁瓣电平优化的同时,能在指定旁瓣区域完成零陷凹面设计,降低有源干扰影响。

For the problems of sparse planar array optimization with side-lobe concave nulls constraints and premature algorithm,a Hybrid Trigonometric Mutation Differential Evolution(HTMDE)algorithm is proposed based on the idea of parameter adaptation.By introducing side-lobe concave nulls constraints matrix,adaptive penalty function is constructed.Time-varying weight combination mutation strategy and crossover strategy improve the initial global search ability and late convergence ability of the algorithm.The constrained optimization of the planar array with peak side lobe level and side-lobe concave nulls is finally realized.The simulation results show that,compared with the algorithm before the hybrid trigonometric mutation strategy,the algorithm not only optimizes the peak side-lobe level of sparse array,but also designs concave nulls in specified side-lobe area to reduce the influence of active interference.