Generalized Pareto ranking bisection for computationally feasible multiobjective antenna optimization

Multiobjective optimization (MO) allows for obtaining comprehensive information about possible design trade‐offs of a given antenna structure. Yet, executing MO using the most popular class of techniques, population‐based metaheuristics, may be computationally prohibitive when full‐wave EM analysis...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of RF and microwave computer-aided engineering Jg. 28; H. 8; S. e21406 - n/a
Hauptverfasser: Unnsteinsson, Sigmar D., Koziel, Slawomir
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken John Wiley & Sons, Inc 01.10.2018
Schlagworte:
Algorithms
antenna design
generalized bisection algorithm
Iterative methods
Local optimization
Monopole antennas
multiobjective optimization
Multiple objective analysis
Parameter identification
Pareto optimization
Pareto ranking
Ranking
simulation‐driven design
variable‐fidelity simulations
ISSN:1096-4290, 1099-047X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Multiobjective optimization (MO) allows for obtaining comprehensive information about possible design trade‐offs of a given antenna structure. Yet, executing MO using the most popular class of techniques, population‐based metaheuristics, may be computationally prohibitive when full‐wave EM analysis is utilized for antenna evaluation. In this work, a low‐cost and fully deterministic MO methodology is introduced. The proposed generalized Pareto ranking bisection algorithm permits identifying a set of Pareto optimal sets of parameters representing the best trade‐offs between considered objectives. The subsequent designs are found by iterative partitioning of the intervals connecting previously obtained designs and executing Pareto‐ranking‐based poll search. The initial approximation of the Pareto front found using the bisection procedure is subsequently refined to the level of the high‐fidelity EM model of the antenna at hand using local optimization. The proposed framework overcomes a serious limitation of the original, recently reported, bisection algorithm, which was only capable of considering two objectives. The generalized version proposed here allows for handling any number of design goals. An improved poll search procedure has also been developed and incorporated. Our algorithm has been demonstrated using two examples of UWB monopole antennas with four figures of interest taken into account: structure size, reflection response, total efficiency, and gain variability.
Bibliographie:Funding information
Icelandic Centre for Research (RANNIS), Grant/Award Number: 163299051; National Science Centre of Poland, Grant/Award Number: 2015/17/B/ST6/01857
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1096-4290
1099-047X
DOI:10.1002/mmce.21406