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.
