Multi-objective Memetic Algorithm with Adaptive Weights for Inverse Antenna Design

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198321" target="_blank" >RIV/00216305:26220/26:0198321 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11074263" target="_blank" >https://ieeexplore.ieee.org/document/11074263</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TAP.2025.3584518" target="_blank" >10.1109/TAP.2025.3584518</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-objective Memetic Algorithm with Adaptive Weights for Inverse Antenna Design

Popis výsledku v původním jazyce

This paper deals with discrete topology optimization and describes the modification of a single-objective algorithm into its multi-objective counterpart. The result is a significant increase in the optimization speed and quality of the resulting Pareto front as compared to conventional state-of-the-art automated inverse design techniques. This advancement is possible thanks to a memetic algorithm combining a gradient-based search for local minima with heuristic optimization to maintain sufficient diversity. The local algorithm is based on rank-1 perturbations; the global algorithm is NSGA-II. An important advancement is the adaptive weighting of objective functions during optimization. The procedure is tested on four challenging examples dealing with both physical and topological metrics and multi-objective settings. The results are compared with standard techniques, and the superb performance of the proposed technique is reported. The implemented algorithm applies to antenna inverse design problems and is an efficient data miner for machine learning tools.

Název v anglickém jazyce

Multi-objective Memetic Algorithm with Adaptive Weights for Inverse Antenna Design

Popis výsledku anglicky

This paper deals with discrete topology optimization and describes the modification of a single-objective algorithm into its multi-objective counterpart. The result is a significant increase in the optimization speed and quality of the resulting Pareto front as compared to conventional state-of-the-art automated inverse design techniques. This advancement is possible thanks to a memetic algorithm combining a gradient-based search for local minima with heuristic optimization to maintain sufficient diversity. The local algorithm is based on rank-1 perturbations; the global algorithm is NSGA-II. An important advancement is the adaptive weighting of objective functions during optimization. The procedure is tested on four challenging examples dealing with both physical and topological metrics and multi-objective settings. The results are compared with standard techniques, and the superb performance of the proposed technique is reported. The implemented algorithm applies to antenna inverse design problems and is an efficient data miner for machine learning tools.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2026

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION

ISSN

0018-926X

e-ISSN

1558-2221

Svazek periodika

74

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

9-24

Kód UT WoS článku

001669225000016

EID výsledku v databázi Scopus

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