粒子群优化与遗传算法在相控阵综合问题中的比较

D. Boeringer, Douglas H. Werner
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引用次数: 40

摘要

粒子群优化是一种新发明的高性能优化器,它具有几个非常理想的属性,包括基本算法非常容易理解和实现。它在某些方面类似于遗传算法或进化算法,但通常只需要几行代码。实现了一个粒子群优化器,并将其与遗传算法进行了比较,用于远场旁瓣陷波的相控阵合成,使用仅限幅、仅限相位和复杂变细。结果表明,粒子群算法在某些情况下表现更好,而遗传算法在另一些情况下表现更好,这意味着两种方法遍历问题超空间的方式不同。粒子群优化算法虽然简单,但在电磁优化方面表现出良好的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comparison of particle swarm optimization and genetic algorithms for a phased array synthesis problem
Particle swarm optimization is a recently invented high-performance optimizer that possesses several highly desirable attributes, including the fact that the basic algorithm is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but generally requires only a few lines of code. A particle swarm optimizer is implemented and compared to a genetic algorithm for phased array synthesis of a far field sidelobe notch, using amplitude-only, phase-only, and complex tapering. The results show that particle swarm optimization performs better in some cases while genetic algorithms perform better in others, which implies that the two methods traverse the problem hyperspace differently. Although simple, the particle swarm optimizer shows good possibilities for electromagnetic optimization.
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