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Nature-Inspired Computation and Swarm Intelligence. Algorithms, Theory and Applications

  • Book

  • April 2020
  • Elsevier Science and Technology
  • ID: 4894816

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.

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Table of Contents

1. Nature-Inspired Computation and Swarm Intelligence 2. Bat Algorithm and Cuckoo Search Algorithms 3. Firefly Algorithm and Flower Pollination Algorithm 4. Bio-inspired Algorithms: Principles, Implementation and Applications to wireless communicatinon

Part II: Theory and Analysis 5. Mathematical Foundations for Algorithm Analysis 6. Probability Theory for Analysing Nature-Inspired Algorithms 7. Theoretical Framework for Algorithm Analysis

Part III: Applications 8. Tuning Restricted Boltzmann Machines 9. Traveling Salesman Problem: Review and New Results 10. Clustering with Nature Inspired Metaheuristics 11. Bat Algorithm for Feature Selection and White Blood Cell Classification 12. Modular Granular Neural Networks Optimisation using the Firefly Algorithm applied to Time Series Prediction 13. Artificail Intelligence Methods for Music generation: A review and future perspectives 14. Optimized controller design for island microgrid employing non-dominated sorting firefly Algorithm (NSFA) 15. Swarm Robotics: A case study -- Bat robotics 16. Electrical Harmonies estimation in power systems using bat algorithm 17. CSBIIST: Cuckoo Search based intelligent Image segmentation technique 18. Improving Genetic Algorithm Solution's Performance for Optimal Order Allocation in an E-Market with the Pareto Optimal Set 19. Multi-Robot Coordination Through Bio-Inspired Strategies 20. Optimization in Probabilistic Domains: An Engineering Approach


Xin-She Yang School of Science and Technology, Middlesex University, UK. Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022).