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Rapid antenna optimization with restricted sensitivity updates by automated dominant direction identification.

Authors :
Pietrenko-Dabrowska, Anna
Koziel, Slawomir
Source :
Knowledge-Based Systems. May2023, Vol. 268, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

Meticulous tuning of geometry parameters turns pivotal in improving performance of antenna systems. It is more and more often realized using formal optimization methods, which is demonstrably the most efficient way of handling multiple design variables, objectives, and constraints. Although in some cases a need for launching global search arises, a typical design scenario only requires local optimization, especially when a decent initial design can be rendered using engineering experience or parametric studies. At the same time, antenna optimization is typically conducted using full-wave electromagnetic (EM) simulations, which entails considerable computational expenses. In this paper, we introduce a novel procedure for expedited antenna tuning. Its fundamental mechanism is to restrict the antenna response sensitivity updates to the selected dominant directions within the parameter space, determined based on the problem-specific knowledge, in particular, the estimated changes of antenna characteristics when moving across one-dimensional affine subspaces spanned by these directions. Thus, the said dominant directions affect the most the responses of the antenna structure under design as assessed using the introduced metrics. The decision making process concerning the number of directions to be used relies on quantification of the aggregated system response variability metrics. The proposed approach is demonstrated by means of several antenna structures and benchmarked against conventional trust-region algorithm, but also its accelerated versions. The results indicate considerable (up to over 60%) speedup over the reference procedure without noticeable quality degradation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09507051
Volume :
268
Database :
Academic Search Index
Journal :
Knowledge-Based Systems
Publication Type :
Academic Journal
Accession number :
163001775
Full Text :
https://doi.org/10.1016/j.knosys.2023.110453