- 210 pages
- English
- PDF
- Available on iOS & Android
About This Book
Swarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then:
- Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possible
- Discusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizers
- Depicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function design
- Details the similarities, differences, weaknesses, and strengths of each swarm optimization method
- Draws parallels between the operators and searching manners of the different algorithms
Swarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB® package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.
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Table of contents
- Front Cover
- Contents
- List of Figures
- List of Tables
- Preface
- 1. Introduction
- 2. Bat Algorithm (BA)
- 3. Artificial Fish Swarm
- 4. Cuckoo Search Algorithm
- 5. Firefly Algorithm (FFA)
- 6. Flower Pollination Algorithm
- 7. Artificial Bee Colony Optimization
- 8. Wolf-Based Search Algorithms
- 9. Bird's-Eye View
- Back Cover