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Multi-objective Design Of Antennas Using Surrogate Models [Hardback]

(Reykjavik Univ, Iceland), (Reykjavik Univ, Iceland)
  • Formāts: Hardback, 360 pages
  • Izdošanas datums: 13-Jan-2017
  • Izdevniecība: World Scientific Europe Ltd
  • ISBN-10: 1786341476
  • ISBN-13: 9781786341471
  • Hardback
  • Cena: 147,05 €
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  • Formāts: Hardback, 360 pages
  • Izdošanas datums: 13-Jan-2017
  • Izdevniecība: World Scientific Europe Ltd
  • ISBN-10: 1786341476
  • ISBN-13: 9781786341471
This book addresses computationally-efficient multi-objective optimization of antenna structures using variable-fidelity electromagnetic simulations, surrogate modeling techniques, and design space reduction methods. Based on contemporary research, it formulates multi-objective design tasks, highlights related challenges in the context of antenna design, and discusses solution approaches. Specific focus is on providing methodologies for handling computationally expensive simulation models of antenna structures in the sense of their multi-objective optimization. Also given is a summary of recent developments in antenna design optimization using variable-fidelity simulation models. Numerous examples of real-world antenna design problems are provided along with discussions and recommendations for the readers interested in applying the considered methods in their design work.Written with researchers and students in mind, topics covered can also be applied across a broad spectrum of aeronautical, mechanical, electrical, biomedical and civil engineering. It is of particular interest to those dealing with optimization, computationally expensive design tasks and simulation-driven design.
Preface vii
About the Authors ix
Acknowledgments xi
1 Introduction
1(8)
2 Simulation-Driven Antenna Design
9(18)
2.1 Simulation-Driven Design of Antenna Structures
9(7)
2.2 Computational Models
16(8)
2.3 Challenges of Contemporary Antenna Design
24(3)
3 Introduction to Numerical Optimization
27(20)
3.1 Optimization Problem Formulation
28(1)
3.2 Gradient-Based Optimization Techniques
29(11)
3.2.1 Gradient-Based Optimization Using Descent Methods
30(3)
3.2.2 Newton and Quasi-Newton Methods
33(3)
3.2.3 Remarks on Constrained Optimization
36(4)
3.3 Derivative-Free Optimization
40(5)
3.3.1 Pattern Search
41(1)
3.3.2 Nelder-Mead Algorithm
42(3)
3.4 Summary
45(2)
4 Global Optimization Using Population-Based Metaheuristics
47(20)
4.1 Introduction to Population-Based Metaheuristics
48(3)
4.2 Evolution Strategies
51(3)
4.3 Genetic Algorithms
54(5)
4.3.1 Algorithm Structure and Representation
54(1)
4.3.2 Crossover
54(2)
4.3.3 Mutation
56(1)
4.3.4 Selection
56(1)
4.3.5 Elitism
57(1)
4.3.6 Selected Topics
58(1)
4.4 Evolutionary Algorithms
59(1)
4.5 Particle Swarm Optimization
60(2)
4.6 Differential Evolution
62(2)
4.7 Other Methods
64(1)
4.8 Summary
64(3)
5 Surrogate-Based Modeling and Optimization
67(34)
5.1 Surrogate-Based Optimization: Brief Introduction
67(4)
5.2 Surrogate Modeling: Data-Driven Surrogates
71(10)
5.2.1 Surrogate Modeling Flow
72(1)
5.2.2 Design of Experiments
73(1)
5.2.3 Data-Driven Modeling Techniques
74(6)
5.2.4 Model Validation
80(1)
5.3 Surrogate Modeling: Physics-Based Surrogates
81(5)
5.4 Optimization Using Data-Driven Surrogates
86(5)
5.4.1 Optimization Using Response Surfaces
86(2)
5.4.2 Sequential Approximate Optimization
88(1)
5.4.3 Optimization with Kriging Surrogates: Exploration versus Exploitation
89(2)
5.4.4 Final Comments
91(1)
5.5 Surrogate-Based Optimization Using Physics-Based Surrogates
91(10)
5.5.1 Space Mapping
91(3)
5.5.2 Approximation Model Management Optimization
94(1)
5.5.3 Manifold Mapping
95(1)
5.5.4 Shape Preserving Response Prediction
95(2)
5.5.5 Adaptively Adjusted Design Specifications
97(3)
5.5.6 Summary
100(1)
6 Multi-Objective Optimization
101(20)
6.1 Formulation of Multi-Objective Optimization Problem
102(1)
6.2 Solution Approaches
103(3)
6.3 Weighted Sum Method
106(1)
6.4 Goal Attainment Method
107(1)
6.5 Multi-Objective Evolutionary Algorithms
108(8)
6.5.1 Algorithm Structure and Search Mechanisms
109(1)
6.5.2 Assessment of Individuals
110(1)
6.5.3 Fitness Sharing
110(2)
6.5.4 Selection
112(1)
6.5.5 Elitism
113(1)
6.5.6 Mating Restrictions
114(1)
6.5.7 Stopping Criteria
115(1)
6.6 Other Multi-Objective Metaheuristics
116(2)
6.6.1 Multi-Objective Particle Swarm Optimization
116(1)
6.6.2 Multi-Objective Differential Evolution
117(1)
6.7 Summary
118(3)
7 Multi-Objective Antenna Optimization Using Surrogate Models
121(20)
7.1 Optimization Using Response Surface Approximation Surrogates and Pareto Front Refinement
122(7)
7.1.1 Kriging and Co-Kriging Interpolation
123(2)
7.1.2 Construction of the Response Surface Approximation Surrogate: Obtaining Initial Pareto Set
125(2)
7.1.3 Pareto Set Refinement Using Response Correction
127(1)
7.1.4 Pareto Set Refinement Using Co-Kriging
128(1)
7.1.5 Optimization Flow Summary
128(1)
7.2 Optimization by Means of Pareto Front Exploration
129(5)
7.2.1 Optimization Algorithm
129(1)
7.2.2 Pareto Front Exploration Using Local Response Surface Approximation Models
130(3)
7.2.3 Optimization Flow
133(1)
7.2.4 Alternative Exploration Methods
134(1)
7.3 Optimization Using Sequential Domain Patching
134(5)
7.3.1 Optimization Flow
135(1)
7.3.2 Sequential Domain Patching Algorithm
136(1)
7.3.3 Automated Determination of Patch Sizes
137(1)
7.3.4 Pareto Set Refinement
138(1)
7.4 Summary
139(2)
8 Design Space Reduction Methods
141(14)
8.1 Design Space Reduction for Antenna Design
142(2)
8.2 Space Reduction Using Extreme Pareto-Optimal Designs
144(1)
8.3 Rotational Design Space Reduction Algorithm
145(4)
8.4 Design Space Confinement
149(2)
8.5 Summary
151(4)
9 Multi-Objective Optimization of Antenna Structures: Application Case Studies
155(70)
9.1 Design of Planar Yagi Antenna Using Decomposition
156(7)
9.1.1 Antenna Geometry and Electromagnetic Models
156(2)
9.1.2 Surrogate Models
158(2)
9.1.3 Numerical Results
160(3)
9.2 Design of Ultra-Wideband Monopole Antenna Using Multi-Objective Evolutionary Algorithm and Co-Kriging
163(2)
9.2.1 Antenna Geometry and Design Objectives
163(1)
9.2.2 Electromagnetic Models Setup
164(1)
9.2.3 Numerical Results
164(1)
9.3 Optimization of Dielectric Resonator Antenna Using Design Space Reduction and Multi-Objective Evolutionary Algorithm
165(12)
9.3.1 Antenna Geometry
167(1)
9.3.2 Design Objectives and Antenna Models
168(1)
9.3.3 Design Space Reduction and Surrogate Model Construction
169(2)
9.3.4 Numerical Results
171(2)
9.3.5 Multi-Objective Optimization in Initially Reduced Space
173(4)
9.3.6 Discussion
177(1)
9.4 Design of a 12-Variable Yagi Antenna Using Design Space Reduction and Multi-Objective Evolutionary Algorithm
177(7)
9.4.1 Antenna Description and Design Objectives
178(1)
9.4.2 Antenna Models and Design Space Reduction
179(1)
9.4.3 Numerical Results
179(3)
9.4.4 Measurements
182(2)
9.5 Design of a Monopole Antenna Using Sequential Domain Patching
184(6)
9.5.1 Antenna Description and Design Objectives
184(1)
9.5.2 Antenna Models and Determination of Extreme Pareto Designs
185(1)
9.5.3 Multi-Objective Optimization Using Sequential Domain Patching Algorithm
186(3)
9.5.4 Comparison with Benchmark Techniques
189(1)
9.6 Optimization of Compact Monopole Antenna by Means of Pareto Front Exploration
190(9)
9.6.1 Antenna Description and Design Objectives
190(1)
9.6.2 Antenna Models and Initial Design
191(1)
9.6.3 Numerical Results
192(3)
9.6.4 Comparison with Benchmark Techniques
195(2)
9.6.5 Measurements
197(2)
9.7 Design of a Ultra-Wideband Monopole Antenna Using Sequential Domain Patching Algorithm with Automated Patch Size Selection
199(6)
9.7.1 Antenna Geometry and Design Objectives
199(1)
9.7.2 Antenna Models and Extreme Pareto Designs
200(1)
9.7.3 Numerical Results
200(2)
9.7.4 Comparison with Benchmark Algorithms
202(3)
9.8 Design of a 14-Variable Multi-Input Multi-Output Antenna Using Design Space Reduction and Co-Kriging
205(6)
9.8.1 Antenna Description and Design Objectives
206(1)
9.8.2 Antenna Models and Design Space Reduction
207(1)
9.8.3 Numerical Results
207(2)
9.8.4 Measurements
209(2)
9.9 Optimization of Broadband Quasi-Yagi Antenna Using Multi-Objective Evolutionary Algorithm and Rotational Space Reduction
211(11)
9.9.1 Antenna Description and Design Objectives
212(1)
9.9.2 Design Space Reduction and Kriging Model Construction
213(1)
9.9.3 Numerical Results
214(3)
9.9.4 Experimental Validation
217(5)
9.10 Summary
222(3)
10 Selected Topics and Practical Issues
225(18)
10.1 Scalability of Surrogate-Assisted Multi-Objective Optimization Algorithm
225(9)
10.1.1 Test Cases
226(2)
10.1.2 Numerical Results
228(4)
10.1.3 Analysis of the Algorithm Scalability
232(2)
10.2 Statistical Analysis of Multi-Objective Evolutionary Algorithm-Based Optimization with Kriging Surrogates
234(3)
10.3 Patch Size Setup Trade-Offs for Sequential Domain Patching Algorithm
237(5)
10.3.1 Test Cases
237(2)
10.3.2 Numerical Results
239(1)
10.3.3 Discussion
240(2)
10.4 Summary
242(1)
11 Applications in Other Engineering Disciplines
243(20)
11.1 Multi-Objective Design of Impedance Matching Transformers
243(6)
11.1.1 Compact Microwave Circuits: Design Challenges
244(1)
11.1.2 Transformer Structure and Models
245(1)
11.1.3 Results and Comparisons
246(1)
11.1.4 Discussion
247(2)
11.2 Multi-Objective Optimization of Compact Couplers
249(6)
11.2.1 Coupler Structure and Problem Formulation
250(1)
11.2.2 Low-Fidelity Model Space Mapping Surrogate
251(1)
11.2.3 Optimization Algorithm
252(1)
11.2.4 Numerical Results and Experimental Validation
253(2)
11.3 Multi-Objective Optimization of Transonic Airfoils
255(7)
11.3.1 Transonic Airfoil Shape Problem Formulation
255(1)
11.3.2 Computational Models
256(3)
11.3.3 Case Study and Results
259(3)
11.4 Summary
262(1)
12 Applications of Multi-Objective Optimization
263(22)
12.1 Performance Comparison of Ultra-Wideband Antennas
263(8)
12.1.1 Antenna Comparison Using Pareto Sets
264(2)
12.1.2 Antenna Structures
266(2)
12.1.3 Pareto Fronts Identification Using Sequential Domain Patching
268(2)
12.1.4 Structure Comparison
270(1)
12.2 Performance Comparison of Rectangular Ultra-Wideband Monopoles
271(2)
12.2.1 Antenna Description
271(1)
12.2.2 Multi-Objective-Based Performance Comparison
272(1)
12.3 Optimum Architecture Selection of Compact Impedance Matching Transformers
273(12)
12.3.1 CMRC-Based Miniaturization: Architecture Selection Problem
273(3)
12.3.2 Generation of Pareto Fronts
276(3)
12.3.3 Numerical Results and Comparisons
279(6)
13 Discussion and Recommendations
285(6)
References 291(24)
Index 315