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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction to Optimization

Abstract
Optimization is part of many university courses because of its importance in many disciplines and applications such as engineering design, business planning, computer science, data mining, machine learning, artificial intelligence and industries. The techniques and algorithms for optimization are diverse, ranging from the traditional gradient-based algorithms to contemporary swarm intelligence based algorithms. This chapter introduces the fundamentals of optimization and some of the traditional optimization techniques.
Xin-She Yang, Xing-Shi He

Chapter 2. Nature-Inspired Algorithms

Abstract
The literature of nature-inspired algorithms and swarm intelligence is expanding rapidly, here we will introduce some of the most recent and widely used nature-inspired optimization algorithms.
Xin-She Yang, Xing-Shi He

Chapter 3. Mathematical Foundations

Abstract
Before we proceed to analyse any nature-inspired algorithms from at least ten different perspectives, let us review the mathematical fundamentals concerning convergence, stability and probability distributions.
Xin-She Yang, Xing-Shi He

Chapter 4. Mathematical Analysis of Algorithms: Part I

Abstract
With the introduction of some of the major nature-inspired algorithms and the brief outline of mathematical foundations, now we are ready to analyse these algorithms in great detail. Obviously, we can analyse these algorithms from different angles and perspectives. Let us first analyse the basic components and mechanisms of most algorithms so as to gain some insights.
Xin-She Yang, Xing-Shi He

Chapter 5. Mathematical Analysis of Algorithms: Part II

Abstract
The perspectives of analysing algorithms we have given so far have been mainly following the mainstream literature on optimization, numerical analysis and operations research. Algorithms can also be analysed from other perspectives from other disciplines such as swarm intelligence, signal and image processing, machine learning, control theory and Bayesian framework.
Xin-She Yang, Xing-Shi He

Chapter 6. Applications of Nature-Inspired Algorithms

Abstract
Nature-inspired algorithms have become powerful and popular for solving problems in optimization, computational intelligence, data mining, machine learning, transport and vehicle routing. After the theoretical analyses in earlier chapters, it would be useful to provide examples and case studies to show that these algorithms can indeed work well in practice.
Xin-She Yang, Xing-Shi He

Backmatter

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