Genetical swarm optimization: Self-adaptive hybrid evolutionary algorithm for electromagnetics
Genetical swarm optimization: Self-adaptive hybrid evolutionary algorithm for electromagnetics
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DOI：
10.1109/tap.2007.891561
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发表时间：
2007-03-01
影响因子：
5.7
通讯作者：
Zich, Riccardo E.
中科院分区：
文献类型：
--
作者：
Grimaccia, Francesco;Mussetta, Marco;Zich, Riccardo E.
提出了一种新的适合于电磁场应用的优化算法--遗传群优化算法（GSO）。这是一种混合算法，开发，以联合收割机在最有效的方式结合两个最流行的进化优化方法，现在用于优化电磁结构，粒子群优化（PSO）和遗传算法（GA）的属性。算法的有效性已在这里进行了测试，它的“祖先”，GA和PSO，处理电磁应用，线性阵列的优化。这里提出的方法表明自己作为一个通用的工具，能够有效地适应不同的电磁优化问题。
A new effective optimization algorithm suitably developed for electromagnetic applications called genetical swarm optimization (GSO) is presented. This is a hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the particle swarm optimization (PSO) and genetic algorithms (GAs). The algorithm effectiveness has been tested here with respect to both its "ancestors," GA and PSO, dealing with an electromagnetic application, the optimization of a linear array. The here proposed method shows itself as a general purpose tool able to effectively adapt itself to different electromagnetic optimization problems.