Emerging Evolutionary Algorithms for Antennas and Wireless Communications

Description

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. Cover 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 Back Cover