团队进步算法与遗传算法和粒子群算法进行天线阵综合的比较  

Synthesis of Antenna Arrays Using Team Progress Algorithm,Genetic Algorithm and Particle Swarm Optimization

作　　者：刘彬[1] 

机构地区：[1]南京邮电大学通信与信息工程学院,南京210003

出　　处：《空间电子技术》2010年第2期76-80,123,共6页Space Electronic Technology

摘　　要：团队进步算法(TPA)是近期由研究者提出的一种新型优化算法,具有与传统优化算法不同的寻优机制。函数测试结果表明,该算法是一种能够兼顾避免早熟收敛和计算速度的有效的优化算法。文章将团队进步算法、遗传算法和粒子群算法应用于阵列天线方向图综合,给定阵列天线合适的设计要求,用Matlab编制程序对阵列天线进行了优化计算。通过对三种优化算法的综合结果比较,表明新算法在应用于较复杂的阵列天线方面以及在优化性能方面的优越性,显示了新算法在天线设计中的广泛应用前景。The team progress algorithm has posed by some researcher recently, which has the search mechanism different from the traditional optimization algorithms. Function tests statistic show that the TPA is a valid optimization algorithm in avoiding premature and the speed of computation, the team progress algorithm, genetic algorithm and particle swarm optimization are applied to the problem of antenna arrays, giving the appropriate designing request. We use Matlab to calculate the antenna arrays. Compared the beam forms attained form team progress algorithm with from genetic algorithm and particle swarm optimization, showing that the novel evolutionary algorithm can apply to complex antenna problem, and the superiority in performance of optimization. All these show the abroad application foreground of novel algorithm in antenna designing.

关 键 词：阵列天线 方向性图 团队进步算法 遗传算法 粒子群算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]