Military antenna design using simple and competent genetic algorithms

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.

[1]  G. Burke,et al.  Numerical Electromagnetics Code (NEC)-Method of Moments. A User-Oriented Computer Code for Analysis of the Electromagnetic Response of Antennas and Other Metal Structures. Part 1: Program Description-Theory. Part 2: Program Description-Code. Volume 1. Revised , 1981 .

[2]  E. Altshuler,et al.  An ultrawide-band impedance-loaded genetic antenna , 2004, IEEE Transactions on Antennas and Propagation.

[3]  Ronald A. Howard,et al.  Readings on the Principles and Applications of Decision Analysis , 1989 .

[4]  Yahya Rahmat-Samii,et al.  Electromagnetic Optimization by Genetic Algorithms , 1999 .

[5]  David E. Goldberg,et al.  Genetic Algorithms and Walsh Functions: Part II, Deception and Its Analysis , 1989, Complex Syst..

[6]  E. Altshuler.  Electrically small self-resonant wire antennas optimized using a genetic algorithm , 2002 .

[7]  E. Cantu-Paz,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1997, Evolutionary Computation.

[8]  Randy L. Haupt,et al.  EXPERIMENTAL ADAPTIVE NULLING WITH A GENETIC ALGORITHM , 1999 .

[9]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[10]  R. J. Mailloux,et al.  A low-sidelobe partially overlapped constrained feed network for time-delayed subarrays , 2001 .

[11]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[12]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[13]  T. H. O'Donnell,et al.  Genetic Antenna Optimization: A Tale of Two Chromosomes , 2003 .

[14]  David E. Goldberg,et al.  Hierarchical Problem Solving and the Bayesian Optimization Algorithm , 2000, GECCO.

[15]  S. P. Skobelev,et al.  Analysis and synthesis of an antenna array with sectoral partial radiation patterns , 1990 .

[16]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[17]  D. Goldberg,et al.  Analysis of Mixing in Genetic Algorithms: A Survey , 2002 .

[18]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  David E. Goldberg,et al.  Bayesian optimization algorithm, decision graphs, and Occam's razor , 2001 .

[21]  David E. Goldberg,et al.  Hierarchical BOA Solves Ising Spin Glasses and MAXSAT , 2003, GECCO.

[22]  David E. Goldberg,et al.  Sizing Populations for Serial and Parallel Genetic Algorithms , 1989, ICGA.

[23]  T.H. O'Donnell,et al.  The significance of genetic representation in genetic antenna design , 2003, IEEE Antennas and Propagation Society International Symposium. Digest. Held in conjunction with: USNC/CNC/URSI North American Radio Sci. Meeting (Cat. No.03CH37450).

[24]  Hao Wang,et al.  Introduction to Genetic Algorithms in Electromagnetics , 1995 .

[25]  Ronald A. Howard,et al.  Influence Diagrams , 2005, Decis. Anal..

[26]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[27]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[28]  David E. Goldberg,et al.  Bayesian Optimization Algorithm: From Single Level to Hierarchy , 2002 .

[29]  D. Goldberg,et al.  Escaping hierarchical traps with competent genetic algorithms , 2001 .

[30]  E. E. Altshuler,et al.  Wire-antenna designs using genetic algorithms , 1997 .

[31]  E. E. Altshuler,et al.  Design of a loaded monopole having hemispherical coverage using a genetic algorithm , 1997 .

[32]  E. E. Altshuler.  Design of a vehicular antenna for GPS/Iridium using a genetic algorithm , 2000 .

[33]  Thomas D. LaToza,et al.  On the supply of building blocks , 2001 .

[34]  Martin Pelikan,et al.  Bayesian Optimization Algorithm , 2005 .