自适应动态Meta粒子群优化算法综合多方向图共形阵列
doi: 10.3724/SP.J.1146.2011.01187
Adaptive Dynamic Meta Particle Swarm Optimization Algorithm Synthesizing Multiple-pattern Conformal Array
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摘要: 基于全波仿真得到的广义阵元有源方向图,该文提出一种用于综合多方向图共形阵列的新方法:自适应动态Meta粒子群优化(ADMPSO)算法。在传统Meta粒子群优化(MPSO)算法基础上,定义了优势子群和非优子群的概念,并通过植入非优子群裁减、优势子群规模膨胀以及惯性权重自适应更新等机制,实现了优化过程中多子群的自适应动态调整,全面提高了算法性能。ADMPSO成功用于12元微带锥面共形阵列非赤道面的多方向图综合,综合过程考虑了由共形载体导致的阵元极化指向各异特征,在公共激励存在约束情况下,使阵列同时实现了笔形、平顶,以及余割平方波束总功率方向图,其与该阵列全波数值仿真完全吻合,优化结果和收敛速度相比于其他算法均有显著改善。Abstract: Based on the general element active pattern achieved by full wave simulation, a novel Adaptive Dynamic Meta Particle Swarm Optimization (ADMPSO) algorithm is proposed for synthesizing multiple-pattern conformal array. The dominated subgroup and nondominated subgroup are defined on the basis of traditional Meta particle swarm. Meanwhile, the adaptive dynamic modulating for multiple-subgroup is realized by introducing the downsizing of nondominated subgroup, the dominated subgroup expansion, and the updating for the adaptive inertia weights, which improve the optimization performance of ADMPSO. Furthermore, ADMPSO algorithm is applied to synthesizing multiple-pattern in 12-element microstrip conic sector conformal array in non-equatorial plane successfully, with the polarizing deterioration of the element considered. And the pencil beam, the flat-top beam, and the cosecant squared beam patterns are achieved simultaneously, with the common amplitude of the excitation restricted. The simulation shows that the optimization results and convergent speed are significantly improved compared with other algorithms.
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