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Nature-Inspired Computation and Swarm Intelligence

Algorithms, Theory and Applications

  • 1st Edition - April 9, 2020
  • Latest edition
  • Editor: Xin-She Yang
  • Language: English

Nature-inspired computation and swarm intelligence have become popular and effective tools for solving problems in optimization, computational intelligence, soft computing and da… Read more

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Description

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.

Key features

  • Introduces nature-inspired algorithms and their fundamentals, including: particle swarm optimization, bat algorithm, cuckoo search, firefly algorithm, flower pollination algorithm, differential evolution and genetic algorithms as well as multi-objective optimization algorithms and others
  • Provides a theoretical foundation and analyses of algorithms, including: statistical theory and Markov chain theory on the convergence and stability of algorithms, dynamical system theory, benchmarking of optimization, no-free-lunch theorems, and a generalized mathematical framework
  • Includes a diversity of case studies of real-world applications: feature selection, clustering and classification, tuning of restricted Boltzmann machines, travelling salesman problem, classification of white blood cells, music generation by artificial intelligence, swarm robots, neural networks, engineering designs and others

Readership

Researchers, advanced undergraduate and graduate students in computer science, engineering, optimization, data science, and management science

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

Product details

  • Edition: 1
  • Latest edition
  • Published: April 10, 2020
  • Language: English

About the editor

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Xin-She Yang

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).
Affiliations and expertise
School of Science and Technology, Middlesex University, UK

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