Result: Military antenna design using simple and competent genetic algorithms

Title:
Military antenna design using simple and competent genetic algorithms
Source:
Optimization and Control for Military ApplicationsMathematical and computer modelling. 43(9-10):990-1022
Publisher Information:
Oxford: Elsevier Science, 2006.
Publication Year:
2006
Physical Description:
print, 39 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Méthodes de calcul scientifique (y compris calcul symbolique, calcul algébrique), Methods of scientific computing (including symbolic computation, algebraic computation), Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Optimisation. Problèmes de recherche, Optimization. Search problems, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme rapide, Fast algorithm, Algoritmo rápido, Analyse numérique, Numerical analysis, Análisis numérico, Antenne, Antenna, Antena, Conception, Design, Diseño, Estimation Bayes, Bayes estimation, Estimación Bayes, Guidage, Guidance, Guiado, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Optimisation, Optimization, Optimización, Plan expérience, Experimental design, Plan experiencia, Programmation mathématique, Mathematical programming, Programación matemática, Réseau(arrangement), Array, Red, Algorithme bayésien hiérarchique optimisation, Hierarchical Bayesian optimization algorithm, Calcul évolutionnaire, Evolutionary computation, Competent genetic algorithm, Genetic algorithm (GA), Hierarchical Bayesian optimization algorithm (hBOA), Optimization technique
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Air Force Research Laboratory, Sensors Directorate, Antenna Technology Branch (AFRL/SNHA), 80 Scott Drive, Hanscom AFB, MA 01731, United States
Illinois Genetic Algorithms Laboratory (IlliGAL), Department of General Engineering, University of Illinois at Urbana-Champaign, 104 S Mathews, Urbana, IL 61801, United States
Air Force Office, MIT Lincoln Laboratory, Arinc, Incorporation, 70 Westview Street, Lexington, MA 02173, United States
ISSN:
0895-7177
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics

Operational research. Management
Accession Number:
edscal.17795990
Database:
PASCAL Archive

Further Information

Over the past decade, the Air Force Research Laboratory (AFRL) Antenna Technology Branch at Hanscom AFB has employed the simple genetic algorithm (SGA) as an optimization tool for a wide variety of antenna applications. Over roughly the same period, researchers at the Illinois Genetic Algorithm Laboratory (IlliGAL) at the University of Illinois at Urbana Champaign have developed GA design theory and advanced GA techniques called competent genetic algorithms-GAs that solve hard problems quickly, reliably, and accurately. Recently, under the guidance and direction of the Air Force Office of Scientific Research (AFOSR), the two laboratories have formed a collaboration, the common goal of which is to apply simple, competent, and hybrid GA techniques to challenging antenna problems. This paper is composed of two parts. The first part of this paper summarizes previous research conducted by AFRL at Hanscom for which SGAs were implemented to obtain acceptable solutions to several antenna problems. This research covers diverse areas of interest, including array pattern synthesis, antenna test-bed design, gain enhancement, electrically small single bent wire elements, and wideband antenna elements. The second part of this paper starts by briefly reviewing the design theory and design principles necessary for the invention and implementation of fast, scalable genetic algorithms. A particular procedure, the hierarchical Bayesian optimization algorithm (hBOA) is then briefly outlined, and the remainder of the paper describes collaborative efforts of AFRL and IlliGAL to solve more difficult antenna problems. In particular, recent results of using hBOA to optimize a novel, wideband overlapped subarray system to achieve -35 dB sidelobes over a 20% bandwidth. The problem was sufficiently difficult that acceptable solutions were not obtained using SGAs. The case study demonstrates the utility of using more advanced GA techniques to obtain acceptable solution quality as problem difficulty increases.