Overview
- Analyzes nature-inspired algorithms
- Provides a unified framework of mathematical analysis for convergence and stability
- Features methods and techniques for selecting specific algorithms
Part of the book series: SpringerBriefs in Optimization (BRIEFSOPTI)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (6 chapters)
Keywords
- General Formulation of Optimization
- Essence of an Algorithm.
- Unconstrained Optimization
- Gradient-Based Optimization Techniques
- Gradient-Free Methods and Metaheuristics
- Nature-Inspired Algorithms
- Stability of an Algorithm
- Algorithm Analysis
- Markov Chain Monte Carlo
- Bayesian Framework
- Swarm Intelligence
- Ant Colony Optimization
- Particle Swarm Optimization
- Bees-inspired Algorithms
- Bat Algorithm
- Firefly Algorithm
- Cuckoo Search
- Hyper-Optimization
- Parameter Tuning and Control
- Filter Theory
About this book
This book presents a systematic approach to analyze nature-inspired algorithms. Beginning with an introduction to optimization methods and algorithms, this book moves on to provide a unified framework of mathematical analysis for convergence and stability. Specific nature-inspired algorithms include: swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithm, firefly algorithm, and cuckoo search. Algorithms are analyzed from a wide spectrum of theories and frameworks to offer insight to the main characteristics of algorithms and understand how and why they work for solving optimization problems. In-depth mathematical analyses are carried out for different perspectives, including complexity theory, fixed point theory, dynamical systems, self-organization, Bayesian framework, Markov chain framework, filter theory, statistical learning, and statistical measures. Students and researchers in optimization, operations research, artificial intelligence, data mining, machine learning, computer science, and management sciences will see the pros and cons of a variety of algorithms through detailed examples and a comparison of algorithms.
Authors and Affiliations
Bibliographic Information
Book Title: Mathematical Foundations of Nature-Inspired Algorithms
Authors: Xin-She Yang, Xing-Shi He
Series Title: SpringerBriefs in Optimization
DOI: https://doi.org/10.1007/978-3-030-16936-7
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-16935-0Published: 20 May 2019
eBook ISBN: 978-3-030-16936-7Published: 08 May 2019
Series ISSN: 2190-8354
Series E-ISSN: 2191-575X
Edition Number: 1
Number of Pages: XI, 107
Number of Illustrations: 2 b/w illustrations, 2 illustrations in colour
Topics: Optimization, Numerical Analysis, Markov model, Algorithms