000			03665 a2200193 4500
999			_c37754 _d37754
001			nam a22 7a 4500
020			_a9780128197141
082			_a006.38 NA TU
245			_aNature-inspired computation and swarm intelligence : _balgorithms, theory and applications _cXin-She Yang
260			_aLondon : _bAcademic Press, _cc2020.
300			_axxiii, 417 p. : _bill. ; _c24 cm.
500			_aIncludes index.
505			_aFront 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
650			_aNatural computation _935190
650			_aSwarm intelligence _932019
700			_aYang, Xin-She, _eEdited by _939168
856			_uhttps://uowd.box.com/s/vputfhdosh2virihawmxbswm3z4qgk21 _zLocation Map
942			_cREGULAR