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

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Yang, Xin-She (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2020.
Temas:
Natural computation.
Swarm intelligence.
Calcul naturel.
Natural computation
Swarm intelligence
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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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