Nature-Inspired Optimization Algorithms

✍ Scribed by Xin-She Yang (Auth.)

• Publisher: Elsevier
• Year: 2014
• Tongue: English
• Leaves: 258
• Series: Elsevier Insights
• Edition: 1
• Category: Library

✦ Synopsis

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization.

This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.

• Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature
• Provides a theoretical understanding as well as practical implementation hints
• Provides a step-by-step introduction to each algorithm

✦ Table of Contents

Content:
Nature-Inspired Optimization Algorithms, Page i
Nature-Inspired Optimization Algorithms, Page iii
Copyright, Page iv
Preface, Pages xi-xii
Chapter 1 - Introduction to Algorithms, Pages 1-21
Chapter 2 - Analysis of Algorithms, Pages 23-44
Chapter 3 - Random Walks and Optimization, Pages 45-65
Chapter 4 - Simulated Annealing, Pages 67-75
Chapter 5 - Genetic Algorithms, Pages 77-87
Chapter 6 - Differential Evolution, Pages 89-97
Chapter 7 - Particle Swarm Optimization, Pages 99-110
Chapter 8 - Firefly Algorithms, Pages 111-127
Chapter 9 - Cuckoo Search, Pages 129-139
Chapter 10 - Bat Algorithms, Pages 141-154
Chapter 11 - Flower Pollination Algorithms, Pages 155-173
Chapter 12 - A Framework for Self-Tuning Algorithms, Pages 175-182
Chapter 13 - How to Deal with Constraints, Pages 183-196
Chapter 14 - Multi-Objective Optimization, Pages 197-211
Chapter 15 - Other Algorithms and Hybrid Algorithms, Pages 213-226
Appendix A - Test Function Benchmarks for Global Optimization, Pages 227-245
Appendix B - Matlab Programs, Pages 247-263