Genetic Algorithms in Electromagnetics / Wiley - IEEE Bd.1 (PDF)
(Sprache: Englisch)
A thorough and insightful introduction to using genetic
algorithms to optimize electromagnetic systems
Genetic Algorithms in Electromagnetics focuses on
optimizing the objective function when a computer algorithm,
analytical model, or experimental...
algorithms to optimize electromagnetic systems
Genetic Algorithms in Electromagnetics focuses on
optimizing the objective function when a computer algorithm,
analytical model, or experimental...
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A thorough and insightful introduction to using genetic
algorithms to optimize electromagnetic systems
Genetic Algorithms in Electromagnetics focuses on
optimizing the objective function when a computer algorithm,
analytical model, or experimental result describes the performance
of an electromagnetic system. It offers expert guidance to
optimizing electromagnetic systems using genetic algorithms (GA),
which have proven to be tenacious in finding optimal results where
traditional techniques fail.
Genetic Algorithms in Electromagnetics begins with an
introduction to optimization and several commonly used numerical
optimization routines, and goes on to feature:
* Introductions to GA in both binary and continuous variable
forms, complete with examples of MATLAB(r) commands
* Two step-by-step examples of optimizing antenna arrays as well
as a comprehensive overview of applications of GA to antenna array
design problems
* Coverage of GA as an adaptive algorithm, including adaptive and
smart arrays as well as adaptive reflectors and crossed
dipoles
* Explanations of the optimization of several different wire
antennas, starting with the famous "crooked monopole"
* How to optimize horn, reflector, and microstrip patch antennas,
which require significantly more computing power than wire
antennas
* Coverage of GA optimization of scattering, including scattering
from frequency selective surfaces and electromagnetic band gap
materials
* Ideas on operator and parameter selection for a GA
* Detailed explanations of particle swarm optimization and
multiple objective optimization
* An appendix of MATLAB code for experimentation
algorithms to optimize electromagnetic systems
Genetic Algorithms in Electromagnetics focuses on
optimizing the objective function when a computer algorithm,
analytical model, or experimental result describes the performance
of an electromagnetic system. It offers expert guidance to
optimizing electromagnetic systems using genetic algorithms (GA),
which have proven to be tenacious in finding optimal results where
traditional techniques fail.
Genetic Algorithms in Electromagnetics begins with an
introduction to optimization and several commonly used numerical
optimization routines, and goes on to feature:
* Introductions to GA in both binary and continuous variable
forms, complete with examples of MATLAB(r) commands
* Two step-by-step examples of optimizing antenna arrays as well
as a comprehensive overview of applications of GA to antenna array
design problems
* Coverage of GA as an adaptive algorithm, including adaptive and
smart arrays as well as adaptive reflectors and crossed
dipoles
* Explanations of the optimization of several different wire
antennas, starting with the famous "crooked monopole"
* How to optimize horn, reflector, and microstrip patch antennas,
which require significantly more computing power than wire
antennas
* Coverage of GA optimization of scattering, including scattering
from frequency selective surfaces and electromagnetic band gap
materials
* Ideas on operator and parameter selection for a GA
* Detailed explanations of particle swarm optimization and
multiple objective optimization
* An appendix of MATLAB code for experimentation
Inhaltsverzeichnis zu „Genetic Algorithms in Electromagnetics / Wiley - IEEE Bd.1 (PDF)“
Preface. Acknowledgments. 1. Introduction to Optimization in Electromagnetics. 1.1 Optimizing a Function of One Variable. 1.1.1 Exhaustive Search. 1.1.2 Random Search. 1.1.3 Golden Search. 1.1.4 Newton's Method. 1.1.5 Quadratic Interpolation. 1.2 Optimizing a Function of Multiple Variables. 1.2.1 Random Search. 1.2.2 Line Search. 1.2.3 Nelder-Mead Downhill Simplex Algorithm. 1.3 Comparing Local Numerical Optimization Algorithms. 1.4 Simulated Annealing. 1.5 Genetic Algorithm. 2. Anatomy of a Genetic Algorithm. 2.1 Creating an Initial Population. 2.2 Evaluating Fitness. 2.3 Natural Selection. 2.4 Mate Selection. 2.4.1 Roulette Wheel Selection. 2.4.2 Tournament Selection. 2.5 Generating Offspring. 2.6 Mutation. 2.7 Terminating the Run. 3. Step-by-Step Examples. 3.1 Placing Nulls. 3.2 Thinned Arrays. 4. Optimizing Antenna Arrays. 4.1 Optimizing Array Amplitude Tapers. 4.2 Optimizing Array Phase Tapers. 4.2.1 Optimum Quantized Low-Sidelobe Phase Tapers. 4.2.2 Phase-Only Array Synthesis Using Adaptive GAs. 4.3 Optimizing Arrays with Complex Weighting. 4.3.1 Shaped-Beam Synthesis. 4.3.2 Creating a Plane Wave in the Near Field. 4.4 Optimizing Array Element Spacing. 4.4.1 Thinned Arrays. 4.4.2 Interleaved Thinned Linear Arrays. 4.4.3 Array Element Perturbation. 4.4.4 Aperiodic Fractile Arrays. 4.4.5 Fractal-Random and Polyfractal Arrays. 4.4.6 Aperiodic Refl ectarrays. 4.5 Optimizing Conformal Arrays. 4.6 Optimizing Reconfi gurable Apertures. 4.6.1 Planar Reconfi gurable Cylindrical Wire Antenna Design. 4.6.2 Planar Reconfi gurable Ribbon Antenna Design. 4.6.3 Design of Volumetric Reconfi gurable Antennas. 4.6.4 Simulation Results--Planar Reconfi gurable Cylindrical Wire Antenna. 4.6.5 Simulation Results--Volumetric Reconfi gurable Cylindrical Wire Antenna. 4.6.6 Simulation Results--Planar Reconfi gurable Ribbon Antenna. 5. Smart Antennas Using a GA. 5.1 Amplitude and Phase Adaptive Nulling. 5.2 Phase-Only Adaptive Nulling. 5.3 Adaptive Reflector. 5.4 Adaptive Crossed
... mehr
Dipoles. 6. Genetic Algorithm Optimization of Wire Antennas. 6.1 Introduction. 6.2 GA Design of Electrically Loaded Wire Antennas. 6.3 GA Design of Three-Dimensional Crooked-Wire Antennas. 6.4 GA Design of Planar Crooked-Wire and Meander-Line Antennas. 6.5 GA Design of Yagi-Uda Antennas. 7. Optimization of Aperture Antennas. 7.1 Refl ector Antennas. 7.2 Horn Antennas. 7.3 Microstrip Antennas. 8. Optimization of Scattering. 8.1 Scattering from an Array of Strips. 8.2 Scattering from Frequency-Selective Surfaces. 8.2.1 Optimization of FSS Filters. 8.2.2 Optimization of Reconfi gurable FSSs. 8.2.3 Optimization of EBGs. 8.3 Scattering from Absorbers. 8.3.1 Conical or Wedge Absorber Optimization. 8.3.2 Multilayer Dielectric Broadband Absorber Optimization. 8.3.3 Ultrathin Narrowband Absorber Optimization. 9. GA Extensions. 9.1 Selecting Population Size and Mutation Rate. 9.2 Particle Swarm Optimization (PSO). 9.3 Multiple-Objective Optimization. 9.3.1 Introduction. 9.3.2 Strength Pareto Evolutionary Algorithm--Strength Value Calculation. 9.3.3 Strength Pareto Evolutionary Algorithm--Pareto Set Clustering. 9.3.4 Strength Pareto Evolutionary Algorithm--Implementation. 9.3.5 SPEA-Optimized Planar Arrays. 9.3.6 SPEA-Optimized Planar Polyfractal Arrays. Appendix: MATLAB® Code. Bibliography. Index.
... weniger
Autoren-Porträt von Randy L. Haupt, Douglas H. Werner
Randy L. Haupt, PhD, is a senior scientist in the AppliedResearch Laboratory at The Pennsylvania State University.
Douglas H. Werner, PhD, is a professor of electrical
engineering, a senior scientist in the Applied Research Laboratory,
and a faculty member of the Materials Research Institute at The
Pennsylvania State University.
Bibliographische Angaben
- Autoren: Randy L. Haupt , Douglas H. Werner
- 2007, 1. Auflage, 320 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 0470106271
- ISBN-13: 9780470106273
- Erscheinungsdatum: 20.08.2007
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
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