Nature-inspired computation and swarm intelligence : algorithms, theory and applications Xin-She Yang - London : Academic Press, c2020. - xxiii, 417 p. : ill. ; 24 cm.
Includes index.
Front Cover --
Nature-Inspired Computation and Swarm Intelligence --
Copyright --
Contents --
List of contributors --
About the editor --
Preface --
Acknowledgments --
Part 1 Algorithms --
1 Nature-inspired computation and swarm intelligence: a state-of-the-art overview --
1.1 Introduction --
1.2 Optimization and optimization algorithms --
1.2.1 Mathematical formulations --
1.2.2 Gradient-based algorithms --
1.2.3 Gradient-free algorithms --
1.3 Nature-inspired algorithms for optimization --
1.3.1 Genetic algorithms --
1.3.2 Ant colony optimization --
1.3.3 Differential evolution 1.3.4 Particle swarm optimization --
1.3.5 Fire y algorithm --
1.3.6 Cuckoo search --
1.3.7 Bat algorithm --
1.3.8 Flower pollination algorithm --
1.3.9 Other algorithms --
1.4 Algorithms and self-organization --
1.4.1 Algorithmic characteristics --
1.4.2 Comparison with traditional algorithms --
1.4.3 Self-organized systems --
1.5 Open problems for future research --
References --
2 Bat algorithm and cuckoo search algorithm --
2.1 Introduction --
2.2 Bat algorithm --
2.2.1 Algorithmic equations of BA --
2.2.2 Pulse emission and loudness --
2.2.3 Pseudocode and parameters 2.2.4 Demo implementation --
2.3 Cuckoo search algorithm --
2.3.1 Cuckoo search --
2.3.2 Pseudocode and parameters --
2.3.3 Demo implementation --
2.4 Discretization and solution representations --
References --
3 Fire y algorithm and ower pollination algorithm --
3.1 Introduction --
3.2 The re y algorithm --
3.2.1 Algorithmic equations in FA --
3.2.2 FA pseudocode --
3.2.3 Scalings and parameters --
3.2.4 Demo implementation --
3.2.5 Multiobjective FA --
3.3 Flower pollination algorithm --
3.3.1 FPA pseudocode and parameters --
3.3.2 Demo implementation --
3.4 Constraint handling 3.5 Applications --
References --
4 Bio-inspired algorithms: principles, implementation, and applications to wireless communication --
4.1 Introduction --
4.2 Selected bio-inspired techniques: principles and implementation --
4.2.1 Genetic algorithm --
4.2.2 Differential evolution --
4.2.3 Particle swarm optimization --
4.2.4 Bacterial foraging optimization --
4.3 Application of bio-inspired optimization techniques in wireless communication --
4.3.1 Bio-inspired techniques for direct modeling application --
4.3.2 Bio-inspired techniques for inverse modeling application 4.3.3 Bio-inspired techniques for mobility management in cellular networks --
4.3.4 Bio-inspired techniques for cognitive radio-based Internet of Things --
4.4 Conclusion --
References --
Part 2 Theory --
5 Mathematical foundations for algorithm analysis --
5.1 Introduction --
5.2 Optimization and optimality --
5.3 Norms --
5.4 Eigenvalues and eigenvectors --
5.5 Convergence sequences --
5.6 Series --
5.7 Computational complexity --
5.8 Convexity --
References --
6 Probability theory for analyzing nature-inspired algorithms --
6.1 Introduction --
6.2 Random variables and probability
9780128197141
Natural computation
Swarm intelligence
006.38 NA TU
Includes index.
Front Cover --
Nature-Inspired Computation and Swarm Intelligence --
Copyright --
Contents --
List of contributors --
About the editor --
Preface --
Acknowledgments --
Part 1 Algorithms --
1 Nature-inspired computation and swarm intelligence: a state-of-the-art overview --
1.1 Introduction --
1.2 Optimization and optimization algorithms --
1.2.1 Mathematical formulations --
1.2.2 Gradient-based algorithms --
1.2.3 Gradient-free algorithms --
1.3 Nature-inspired algorithms for optimization --
1.3.1 Genetic algorithms --
1.3.2 Ant colony optimization --
1.3.3 Differential evolution 1.3.4 Particle swarm optimization --
1.3.5 Fire y algorithm --
1.3.6 Cuckoo search --
1.3.7 Bat algorithm --
1.3.8 Flower pollination algorithm --
1.3.9 Other algorithms --
1.4 Algorithms and self-organization --
1.4.1 Algorithmic characteristics --
1.4.2 Comparison with traditional algorithms --
1.4.3 Self-organized systems --
1.5 Open problems for future research --
References --
2 Bat algorithm and cuckoo search algorithm --
2.1 Introduction --
2.2 Bat algorithm --
2.2.1 Algorithmic equations of BA --
2.2.2 Pulse emission and loudness --
2.2.3 Pseudocode and parameters 2.2.4 Demo implementation --
2.3 Cuckoo search algorithm --
2.3.1 Cuckoo search --
2.3.2 Pseudocode and parameters --
2.3.3 Demo implementation --
2.4 Discretization and solution representations --
References --
3 Fire y algorithm and ower pollination algorithm --
3.1 Introduction --
3.2 The re y algorithm --
3.2.1 Algorithmic equations in FA --
3.2.2 FA pseudocode --
3.2.3 Scalings and parameters --
3.2.4 Demo implementation --
3.2.5 Multiobjective FA --
3.3 Flower pollination algorithm --
3.3.1 FPA pseudocode and parameters --
3.3.2 Demo implementation --
3.4 Constraint handling 3.5 Applications --
References --
4 Bio-inspired algorithms: principles, implementation, and applications to wireless communication --
4.1 Introduction --
4.2 Selected bio-inspired techniques: principles and implementation --
4.2.1 Genetic algorithm --
4.2.2 Differential evolution --
4.2.3 Particle swarm optimization --
4.2.4 Bacterial foraging optimization --
4.3 Application of bio-inspired optimization techniques in wireless communication --
4.3.1 Bio-inspired techniques for direct modeling application --
4.3.2 Bio-inspired techniques for inverse modeling application 4.3.3 Bio-inspired techniques for mobility management in cellular networks --
4.3.4 Bio-inspired techniques for cognitive radio-based Internet of Things --
4.4 Conclusion --
References --
Part 2 Theory --
5 Mathematical foundations for algorithm analysis --
5.1 Introduction --
5.2 Optimization and optimality --
5.3 Norms --
5.4 Eigenvalues and eigenvectors --
5.5 Convergence sequences --
5.6 Series --
5.7 Computational complexity --
5.8 Convexity --
References --
6 Probability theory for analyzing nature-inspired algorithms --
6.1 Introduction --
6.2 Random variables and probability
9780128197141
Natural computation
Swarm intelligence
006.38 NA TU