Nature-Inspired Computation and Swarm Intelligence

Algorithms, Theory and Applications

Xin-She Yang

• Publisher: Academic Press
• Year: 2020
• Print ISBN: 9780128197141
• eBook ISBN: 9780128226094
• Topic: Technology & Engineering
• Subtopic: Business Intelligence
• Index: Technology & Engineering

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.- 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

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• About the editor
• Preface
• Acknowledgments
• Part 1: Algorithms
• Chapter 1: Nature-inspired computation and swarm intelligence: a state-of-the-art overview
• Chapter 2: Bat algorithm and cuckoo search algorithm
• Chapter 3: Firefly algorithm and flower pollination algorithm
• Chapter 4: Bio-inspired algorithms: principles, implementation, and applications to wireless communication
• Part 2: Theory
• Chapter 5: Mathematical foundations for algorithm analysis
• Chapter 6: Probability theory for analyzing nature-inspired algorithms
• Chapter 7: Mathematical framework for algorithm analysis
• Part 3: Applications
• Chapter 8: Fine-tuning restricted Boltzmann machines using quaternion-based flower pollination algorithm
• Chapter 9: Traveling salesman problem: a perspective review of recent research and new results with bio-inspired metaheuristics
• Chapter 10: Clustering with nature-inspired metaheuristics
• Chapter 11: Bat-inspired algorithm for feature selection and white blood cell classification
• Chapter 12: Modular granular neural network optimization using the firefly algorithm applied to time series prediction
• Chapter 13: Artificial intelligence methods for music generation: a review and future perspectives
• Chapter 14: Optimized controller design for islanded microgrid employing nondominated sorting firefly algorithm
• Chapter 15: Swarm robotics – a case study: bat robotics
• Chapter 16: Electrical harmonics estimation in power systems using bat algorithm
• Chapter 17: CSBIIST: cuckoo search-based intelligent image segmentation technique
• Chapter 18: Improving genetic algorithm solution performance for optimal order allocation in an e-market with the Pareto-optimal set
• Chapter 19: Multirobot coordination through bio-inspired strategies
• Chapter 20: Optimization in probabilistic domains: an engineering approach
• Index