.png){kind=icon}
返回
Improved GA and PSO culled hybrid algorithm for antenna array pattern synthesis
.png){kind=icon}
OA
AI
DOI：10.2528/PIER07121503.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a new evolutionary learning algorithm based on a hybrid of improved real-code genetic algorithm (IGA) and particle swarm optimization (PSO) called HIGAPSO is proposed. In order to overcome the drawbacks of standard genetic algorithm and particle swarm optimization, some improved mechanisms based on non-linear ranking selection, competition and selection among several crossover offspring and adaptive change of mutation scaling are adopted in the genetic algorithm, and dynamical parameters are adopted in PSO. The new population is produced through three approaches to improve the global optimization performance, which are elitist strategy, PSO strategy and improved genetic algorithm ( IGA) strategy. The effectiveness of the proposed algorithm has been compared with GAs and PSO, synthesizing a circular array, a linear array and a base station array. Results show that the proposed algorithm is able to adapt itself to different electromagnetic optimization problems more effectively.
Keyword:
PARTICLE SWARM OPTIMIZATION
CLONAL SELECTION ALGORITHM
GENETIC ALGORITHMS
SIDELOBE LEVEL
NULL
DESIGN
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
P
IF:
9.3
论文数:
2.9K
被引数:
2.7K
机构
引用论文
.png){kind=icon}
分享收藏Variation of series resistance with temperature and illumination level in diffused junction poly- and single-crystalline silicon solar cells扩散结多晶硅和单晶硅太阳能电池中串联电阻随温度和照度的变化
PREAI
分享收藏
查看更多内容