| Description |
1 online resource |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Note |
Includes index. |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Subject |
Natural computation.
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Swarm intelligence.
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Calcul naturel.
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Natural computation
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Swarm intelligence
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| Added Author |
Yang, Xin-She, editor.
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| Other Form: |
Print version: 0128197145 9780128197141 (OCoLC)1130248742 |
| ISBN |
9780128226094 (electronic bk.) |
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0128226099 (electronic bk.) |
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9780128197141 |
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0128197145 |
| Standard No. |
AU@ 000067165108 |
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AU@ 000068128303 |
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CHDSB 007190441 |
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CHVBK 588855529 |
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