2015
DOI: 10.1109/map.2015.2437277
|View full text |Cite
|
Sign up to set email alerts
|

Improved Electromagnetics Optimization: The covariance matrix adaptation evolutionary strategy

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
9
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
6
3

Relationship

4
5

Authors

Journals

citations
Cited by 32 publications
(9 citation statements)
references
References 40 publications
0
9
0
Order By: Relevance
“…This necessitates the use of an optimization procedure. The Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is a global optimization algorithm that has been shown to be well suited to finding solutions for a wide variety of electromagnetics problems 23,24 . In this case, CMA-ES was paired with HFSS, a commercial computational electromagnetics solver, to optimize the geometry and metasurface characteristics of the A-SBFA, with the goal of maximizing peak directivity at GPS bands L1 (1.575 GHz) and L2 (1.227 GHz).…”
Section: Resultsmentioning
confidence: 99%
“…This necessitates the use of an optimization procedure. The Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is a global optimization algorithm that has been shown to be well suited to finding solutions for a wide variety of electromagnetics problems 23,24 . In this case, CMA-ES was paired with HFSS, a commercial computational electromagnetics solver, to optimize the geometry and metasurface characteristics of the A-SBFA, with the goal of maximizing peak directivity at GPS bands L1 (1.575 GHz) and L2 (1.227 GHz).…”
Section: Resultsmentioning
confidence: 99%
“…Global optimization algorithms are largely able to overcome these issues. While nearly countless examples of global optimizers exist today, algorithms such as the genetic algorithm (GA) [297,298], particle swarm optimization (PSO) [299,300], and the covariance matrix adaptation evolution strategy (CMA-ES) [301][302][303] are among the most popular for optical device optimization [283]. Most global optimizers are population-based evolutionary algorithms which update and adapt a population as they explore a function's response surface to produce new generations better suited to finding the true global minimum.…”
Section: Advanced Design and Optimization: Toward Multifunctional Metmentioning
confidence: 99%
“…A variety of optimizations will be carried out here to highlight the utility of the analytical expressions. CMA-ES [32] has been found to be extremely effective in single-objective optimizations for electromagnetics problems [33][34]. In real-world engineering problems, there are often multiple conflicting objectives, with a classical example being size versus performance.…”
Section: Optimization Examplesmentioning
confidence: 99%