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Nature-inspired computation and swarm intelligence : algorithms, theory and applications

Title By: Yang, Xin-She [Edited by ]
Publisher: London : Academic Press, c2020.Description: xxiii, 417 p. : ill. ; 24 cm.ISBN: 9780128197141Subject(s): Natural computation | Swarm intelligenceDDC classification: 006.38 NA TU Online resources: Location Map
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Item type Home library Call number Status Date due Barcode Item holds Course reserves
REGULAR University of Wollongong in Dubai
Main Collection
006.38 NA TU (Browse shelf) Available T0064801

CSCI323 Winter2024

REGULAR University of Wollongong in Dubai
Main Collection
006.38 NA TU (Browse shelf) Available T0065186
Total holds: 0

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

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