Nature-Inspired Computation and Swarm Intelligence

Algorithms, Theory and Applications

Specificaties
Paperback, blz. | Engels
Elsevier Science | e druk, 2020
ISBN13: 9780128197141
Rubricering
Elsevier Science e druk, 2020 9780128197141
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

Nature-inspired computation and swarm intelligence have become popular and effective tools for solving problems in optimization, computational intelligence, soft computing and data science. Recently, the literature in the field has expanded rapidly, with new algorithms and applications emerging.

Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is a timely reference giving a comprehensive review of relevant state-of-the-art developments in algorithms, theory and applications of nature-inspired algorithms and swarm intelligence. It reviews and documents the new developments, focusing on nature-inspired algorithms and their theoretical analysis, as well as providing a guide to their implementation. The book includes case studies of diverse real-world applications, balancing explanation of the theory with practical implementation.

Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is suitable for researchers and graduate students in computer science, engineering, data science, and management science, who want a comprehensive review of algorithms, theory and implementation within the fields of nature inspired computation and swarm intelligence.

Specificaties

ISBN13:9780128197141
Taal:Engels
Bindwijze:Paperback

Inhoudsopgave

<p>1. Nature-Inspired Computation and Swarm Intelligence<br>2. Bat Algorithm and Cuckoo Search Algorithms<br>3. Firefly Algorithm and Flower Pollination Algorithm<br>4. Bio-inspired Algorithms: Principles, Implementation and Applications to wireless communicatinon</p> <p>Part II: Theory and Analysis<br>5. Mathematical Foundations for Algorithm Analysis<br>6. Probability Theory for Analysing Nature-Inspired Algorithms<br>7. Theoretical Framework for Algorithm Analysis</p> <p>Part III: Applications<br>8. Tuning Restricted Boltzmann Machines<br>9. Traveling Salesman Problem: Review and New Results<br>10. Clustering with Nature Inspired Metaheuristics<br>11. Bat Algorithm for Feature Selection and White Blood Cell Classification<br>12. Modular Granular Neural Networks Optimisation using the Firefly Algorithm applied to Time Series Prediction<br>13. Artificail Intelligence Methods for Music generation: A review and future perspectives<br>14. Optimized controller design for island microgrid employing non-dominated sorting firefly Algorithm (NSFA)<br>15. Swarm Robotics: A case study -- Bat robotics<br>16. Electrical Harmonies estimation in power systems using bat algorithm<br>17. CSBIIST: Cuckoo Search based intelligent Image segmentation technique<br>18. Improving Genetic Algorithm Solution’s Performance for Optimal Order Allocation in an E-Market with the Pareto Optimal Set <br>19. Multi-Robot Coordination Through Bio-Inspired Strategies<br>20. Optimization in Probabilistic Domains: An Engineering Approach</p>

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