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Emerging Evolutionary Algorithms for Antennas and Wireless Communications [Kõva köide]

(Aristotle University of Thessaloniki, Department of Physics, Greece)
  • Formaat: Hardback, 342 pages, kõrgus x laius: 234x156 mm
  • Sari: Electromagnetic Waves
  • Ilmumisaeg: 11-Jun-2021
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 1785615521
  • ISBN-13: 9781785615528
Teised raamatud teemal:
Emerging Evolutionary Algorithms for Antennas and Wireless CommunicationsSuurem pilt
  • Formaat: Hardback, 342 pages, kõrgus x laius: 234x156 mm
  • Sari: Electromagnetic Waves
  • Ilmumisaeg: 11-Jun-2021
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 1785615521
  • ISBN-13: 9781785615528
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781785615528.html
Märksõnad:

Several evolutionary algorithms (EAs) have emerged in recent decades that mimic the behaviour and evolution of biological entities. EAs are widely used to solve single and multi-objective optimization engineering problems. EAs have also been applied to a variety of microwave components, antenna design, radar design, and wireless communications problems. These techniques, among others, include genetic algorithms (GAs), evolution strategies (ES), particle swarm optimization (PSO), differential evolution (DE), and ant colony optimization (ACO). In addition, new innovative algorithms that are not only biology-based but also physics-based or music-based are also emerging, as are hybrid combinations of EAs.

The use of evolutionary algorithms is having an increasing impact on antenna design and wireless communications problems. EAs combined with numerical methods in electromagnetics have obtained significant and successful results.

This book aims to present some of the emerging EAs and their variants. Chapter 1 introduces the optimization methods in general and the evolutionary algorithms. Chapter 2 presents briefly some of the most popular evolutionary algorithms, such as particle swarm optimization (PSO), differential evolution (DE), and ant colony optimization (ACO) as well as some emerging ones. Chapter 3 focuses on antenna array synthesis, which constitutes a wide range of antenna design problems. Chapter 4 gives an overview of patch antenna design using evolutionary algorithms. Chapter 5 presents design cases from different microwave structure cases. Chapter 6 discusses on various representative design problems in wireless communications. Chapter 7 deals with design cases for 5G and beyond.



This book presents some of the emerging evolutionary algorithms (EAs) and their variants. It presents design cases of different EAs applied to popular design problems in antennas and wireless communications. The book contains both cases of single and multi-objective optimization.

About the author xi
Preface xiii
1 Introduction
1(26)
1.1 Optimization algorithms
2(1)
1.1.1 Deterministic algorithms
2(1)
1.1.2 Stochastic algorithms
2(1)
1.2 Evolutionary algorithms
3(6)
1.2.1 Encoding
6(1)
1.2.2 Boundary conditions constraint handling methods
7(1)
1.2.3 The no free lunch theorem
8(1)
1.3 Objective functions
9(4)
1.3.1 Common benchmark functions
11(2)
1.4 Comparison metrics
13(4)
1.4.1 Nonparametric tests
14(1)
1.4.2 Signature of an algorithm
15(2)
1.5 Multi-objective algorithms
17(3)
1.5.1 Fuzzy decision maker
19(1)
1.5.2 Performance indicators for MOEAs
19(1)
1.6 Discussion-open issues
20(2)
References
22(5)
2 Evolutionary Algorithms
27(56)
2.1 Swarm intelligence algorithms
27(12)
2.1.1 Initialization
28(1)
2.1.2 Inertia weight particle swarm optimization
28(1)
2.1.3 Constriction factor particle swarm optimization
29(1)
2.1.4 Comprehensive learning particle swarm optimizer
30(1)
2.1.5 PSO for discrete-valued problems
30(3)
2.1.6 Artificial bee colony algorithm
33(1)
2.1.7 Ant colony optimization
34(1)
2.1.8 Emerging nature-inspired swarm algorithms
34(5)
2.2 Differential evolution
39(8)
2.2.1 Self-adaptive DE algorithms
40(5)
2.2.2 Novel binary differential evolution
45(2)
2.3 Biogeography-based optimization
47(5)
2.3.1 Chaotic BBO
51(1)
2.4 Emerging evolutionary algorithms
52(11)
2.4.1 Biology-based algorithms
53(5)
2.4.2 Physics-based algorithms
58(2)
2.4.3 Human social behavior-based algorithms
60(2)
2.4.4 Music-based algorithms
62(1)
2.5 Opposition-based learning
63(4)
2.5.1 OBL types
64(1)
2.5.2 OBL algorithm description
65(1)
2.5.3 Modified generalized OBBO
66(1)
2.6 Multi-objective algorithms
67(6)
2.6.1 Non-dominated sorting genetic Algorithm-II
67(1)
2.6.2 Non-dominated sorting genetic Algorithm-Ill
68(1)
2.6.3 Generalized differential evolution
69(3)
2.6.4 Speed-constrained multi-objective PSO
72(1)
2.6.5 Multi-objective BBO
73(1)
2.6.6 Computational complexity of MO algorithms
73(1)
References
73(10)
3 Antenna Array Design Using Eas
83(46)
3.1 Linear-array design
83(16)
3.1.1 Position-only optimization
85(4)
3.1.2 Phase-only optimization
89(2)
3.1.3 Position and phase optimization
91(4)
3.1.4 Amplitude-only optimization
95(4)
3.2 Thinned-array design
99(5)
3.3 Shaped beam synthesis
104(3)
3.4 Planar thinned-array design
107(5)
3.5 Conformal array design
112(3)
3.6 Reducing the number of elements in array design
115(9)
3.6.1 20-Element Chebyshev array
116(4)
3.6.2 A 29-element Taylor-Kaiser array
120(4)
References
124(5)
4 Microstrip Patch Antenna Design
129(32)
4.1 E-shaped patch antenna design
129(11)
4.1.1 Frequency-independent design procedure
131(1)
4.1.2 Dual-band 5G antenna design
132(8)
4.2 Half E-shaped patch antenna design
140(12)
4.2.1 Wireless LAN antenna design
140(3)
4.2.2 5G antenna design
143(9)
4.3 Arbitrary-shaped patch antenna design
152(6)
References
158(3)
5 Microwave Structures Design Using Eas
161(68)
5.1 Design of microwave broadband absorbers
161(20)
5.1.1 Problem formulation
161(2)
5.1.2 Single-objective absorber optimization
163(15)
5.1.3 Multi-objective absorber optimization
178(3)
5.2 Dielectric filters design
181(31)
5.2.1 Problem formulation
182(2)
5.2.2 Single-objective optimization of dielectric filters
184(18)
5.2.3 Multi-objective optimization
202(10)
5.3 Microstrip filters design
212(10)
5.3.1 Microstrip band-pass filter
212(2)
5.3.2 Single band open-loop ring resonator filter
214(5)
5.3.3 Dual-band OLRR filter
219(3)
References
222(7)
6 Design Problems In Wireless Communications
229(70)
6.1 Peak-to-average power ratio reduction in OFDM systems
229(10)
6.1.1 System model
229(2)
6.1.2 Simulation settings
231(1)
6.1.3 Tuning control parameters
232(5)
6.1.4 Comparison with other methods
237(2)
6.2 Antenna selection in MIMO systems
239(15)
6.2.1 MIMO system model
240(2)
6.2.2 CBBO algorithm selection
242(4)
6.2.3 Simulation results
246(8)
6.3 Cognitive radio engine design
254(12)
6.3.1 Problem formulation
255(3)
6.3.2 Numerical results
258(8)
6.4 Spectrum allocation in cognitive radio networks
266(13)
6.4.1 Problem formulation
267(4)
6.4.2 Simulation results
271(4)
6.4.3 Asymptotic behavior
275(4)
6.5 Optimization of wireless sensor networks
279(10)
6.5.1 System model
280(3)
6.5.2 Numerical results
283(6)
References
289(10)
7 Design Problems For 5G And Beyond
299(22)
7.1 Multi-objective optimization in 5G massive MIMO wireless networks
299(10)
7.1.1 System model
300(1)
7.1.2 Multi-objective evolutionary algorithm-based solution
301(1)
7.1.3 Proposed optimization framework
301(1)
7.1.4 Numerical results
302(7)
7.2 Joint power allocation and user association in non-orthogonal multiple access networks
309(9)
7.2.1 System model
310(2)
7.2.2 Problem formulation
312(1)
7.2.3 Numerical results
313(5)
References
318(3)
Index 321
Sotirios K. Goudos PhD is a senior member of the IEEE, the IEICE, the Greek Physics Society and the Greek Computer Society. He is an associate professor in the Department of Physics at Aristotle University of Thessaloniki, Greece. His research interests include antenna and microwave structures design, electromagnetic compatibility of communication systems, evolutionary computation algorithms, mobile communications and semantic web technologies. He has published one book and is a member of several journal editorial boards.