Towards Demand-Driven Optimization Algorithms in Electromagnetic Engineering

被引：0

作者：

Kovaleva, Maria ^{[1
]}

Bulger, David ^{[2
]}

Esselle, Karu P. ^{[3
]}

机构：

[1] Macquarie Univ, Sch Engn, Sydney, NSW, Australia

[2] Macquarie Univ, Dept Math & Stat, Sydney, NSW, Australia

[3] Univ Technol Sydney, Sch Elect & Data Engn, Sydney, NSW, Australia

来源：

PROCEEDINGS OF THE 2020 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL ELECTROMAGNETICS (ICCEM 2020) | 2020年

关键词：

optimization; evolutionary; cross-entropy; antenna; electromagnetics; PARTICLE SWARM OPTIMIZATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

With the increasing popularity of optimization algorithms in electromagnetic engineering, it is clear that mixed-variable design problems prevail. This paper shows that the Cross-Entropy (CE) optimization method is intrinsically versatile to handle these and other types of problems. We provide implementation details of two antenna examples optimized by the CE method to demonstrate its elegance and efficiency.

引用

页码：95 / 96

页数：2

共 17 条

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