Emerging Evolutionary Algorithms for Antennas and Wireless Communications

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Contents
About the author
Preface
1 Introduction
	1.1 Optimization algorithms
		1.1.1 Deterministic algorithms
		1.1.2 Stochastic algorithms
	1.2 Evolutionary algorithms
		1.2.1 Encoding
		1.2.2 Boundary conditions constraint handling methods
		1.2.3 The no free lunch theorem
	1.3 Objective functions
		1.3.1 Common benchmark functions
			1.3.1.1 Unimodal functions
			1.3.1.2 Multimodal functions
	1.4 Comparison metrics
		1.4.1 Nonparametric tests
		1.4.2 Signature of an algorithm
	1.5 Multi-objective algorithms
		1.5.1 Fuzzy decision maker
		1.5.2 Performance indicators for MOEAs
	1.6 Discussion-open issues
	References
2 Evolutionary algorithms
	2.1 Swarm intelligence algorithms
		2.1.1 Initialization
		2.1.2 Inertia weight particle swarm optimization
		2.1.3 Constriction factor particle swarm optimization
		2.1.4 Comprehensive learning particle swarm optimizer
		2.1.5 PSO for discrete-valued problems
			2.1.5.1 Binary PSO variants
			2.1.5.2 Boolean PSO
		2.1.6 Artificial bee colony algorithm
			2.1.6.1 Gbest-guidedABC
		2.1.7 Ant colony optimization
		2.1.8 Emerging nature-inspired swarm algorithms
			2.1.8.1 Grey wolf optimizer
			2.1.8.2 Binary GWO versions
			2.1.8.3 Whale optimization algorithm
			2.1.8.4 Salp swarm algorithm
	2.2 Differential evolution
		2.2.1 Self-adaptive DE algorithms
			2.2.1.1 jDE algorithm
			2.2.1.2 Barebones DE
			2.2.1.3 Composite DE
			2.2.1.4 CoDE with eigenvector-based crossover operator (CoDE-EIG)
			2.2.1.5 The SaDE algorithm
			2.2.1.6 The JADE algorithm
		2.2.2 Novel binary differential evolution
	2.3 Biogeography-based optimization
		2.3.1 Chaotic BBO
	2.4 Emerging evolutionary algorithms
		2.4.1 Biology-based algorithms
			2.4.1.1 Firefly algorithm
			2.4.1.2 Monarch butterfly optimization
			2.4.1.3 Greedy strategy and self-adaptive crossover MBO (GCMBO)
			2.4.1.4 Moth search algorithm
			2.4.1.5 Elephant herding optimization
			2.4.1.6 Shuffled frog-leaping algorithm
		2.4.2 Physics-based algorithms
			2.4.2.1 Gravitational search algorithm
			2.4.2.2 PSOGSA
			2.4.2.3 Wind-driven optimization
		2.4.3 Human social behavior-based algorithms
			2.4.3.1 Teaching–learning-based optimization
			2.4.3.2 Jaya
			2.4.3.3 TLBO–Jaya algorithm
		2.4.4 Music-based algorithms
			2.4.4.1 Harmony search algorithm
	2.5 Opposition-based learning
		2.5.1 OBL types
		2.5.2 OBL algorithm description
		2.5.3 Modified generalized OBBO
	2.6 Multi-objective algorithms
		2.6.1 Non-dominated sorting genetic Algorithm-II
			2.6.1.1 Non-dominated ranking
			2.6.1.2 Algorithm description
		2.6.2 Non-dominated sorting genetic Algorithm-III
		2.6.3 Generalized differential evolution
		2.6.4 Speed-constrained multi-objective PSO
		2.6.5 Multi-objective BBO
		2.6.6 Computational complexity of MO algorithms
	References
3 Antenna array design using EAs
	3.1 Linear-array design
		3.1.1 Position-only optimization
		3.1.2 Phase-only optimization
		3.1.3 Position and phase optimization
		3.1.4 Amplitude-only optimization
	3.2 Thinned-array design
	3.3 Shaped beam synthesis
	3.4 Planar thinned-array design
	3.5 Conformal array design
	3.6 Reducing the number of elements in array design
		3.6.1 20-Element Chebyshev array
		3.6.2 A 29-element Taylor–Kaiser array
	References
4 Microstrip patch antenna design
	4.1 E-shaped patch antenna design
		4.1.1 Frequency-independent design procedure
		4.1.2 Dual-band 5G antenna design
	4.2 Half E-shaped patch antenna design
		4.2.1 Wireless LAN antenna design
		4.2.2 5G antenna design
	4.3 Arbitrary-shaped patch antenna design
	References
5 Microwave structures design using EAs
	5.1 Design of microwave broadband absorbers
		5.1.1 Problem formulation
		5.1.2 Single-objective absorber optimization
			5.1.2.1 Statistical analysis of results
		5.1.3 Multi-objective absorber optimization
	5.2 Dielectric filters design
		5.2.1 Problem formulation
		5.2.2 Single-objective optimization of dielectric filters
			5.2.2.1 Statistical analysis of results
		5.2.3 Multi-objective optimization
	5.3 Microstrip filters design
		5.3.1 Microstrip band-pass filter
		5.3.2 Single band open-loop ring resonator filter
		5.3.3 Dual-band OLRR filter
	References
6 Design problems in wireless communications
	6.1 Peak-to-average power ratio reduction in OFDM systems
		6.1.1 System model
		6.1.2 Simulation settings
		6.1.3 Tuning control parameters
		6.1.4 Comparison with other methods
	6.2 Antenna selection in MIMO systems
		6.2.1 MIMO system model
		6.2.2 CBBO algorithm selection
		6.2.3 Simulation results
			6.2.3.1 Nonparametric statistical tests
	6.3 Cognitive radio engine design
		6.3.1 Problem formulation
		6.3.2 Numerical results
	6.4 Spectrum allocation in cognitive radio networks
		6.4.1 Problem formulation
		6.4.2 Simulation results
		6.4.3 Asymptotic behavior
	6.5 Optimization of wireless sensor networks
		6.5.1 System model
		6.5.2 Numerical results
	References
7 Design problems for 5G and beyond
	7.1 Multi-objective optimization in 5G massive MIMO wireless networks
		7.1.1 System model
		7.1.2 Multi-objective evolutionary algorithm-based solution
		7.1.3 Proposed optimization framework
		7.1.4 Numerical results
	7.2 Joint power allocation and user association in non-orthogonal multiple access networks
		7.2.1 System model
		7.2.2 Problem formulation
		7.2.3 Numerical results
	References
Index
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