Book description
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.
Table of contents
- Front Cover
- Contents (1/2)
- Contents (2/2)
- List of Figures
- List of Tables
- Preface
- 1. Introduction (1/3)
- 1. Introduction (2/3)
- 1. Introduction (3/3)
- 2. Bat Algorithm (BA) (1/6)
- 2. Bat Algorithm (BA) (2/6)
- 2. Bat Algorithm (BA) (3/6)
- 2. Bat Algorithm (BA) (4/6)
- 2. Bat Algorithm (BA) (5/6)
- 2. Bat Algorithm (BA) (6/6)
- 3. Artificial Fish Swarm (1/5)
- 3. Artificial Fish Swarm (2/5)
- 3. Artificial Fish Swarm (3/5)
- 3. Artificial Fish Swarm (4/5)
- 3. Artificial Fish Swarm (5/5)
- 4. Cuckoo Search Algorithm (1/5)
- 4. Cuckoo Search Algorithm (2/5)
- 4. Cuckoo Search Algorithm (3/5)
- 4. Cuckoo Search Algorithm (4/5)
- 4. Cuckoo Search Algorithm (5/5)
- 5. Firefly Algorithm (FFA) (1/6)
- 5. Firefly Algorithm (FFA) (2/6)
- 5. Firefly Algorithm (FFA) (3/6)
- 5. Firefly Algorithm (FFA) (4/6)
- 5. Firefly Algorithm (FFA) (5/6)
- 5. Firefly Algorithm (FFA) (6/6)
- 6. Flower Pollination Algorithm (1/3)
- 6. Flower Pollination Algorithm (2/3)
- 6. Flower Pollination Algorithm (3/3)
- 7. Artificial Bee Colony Optimization (1/8)
- 7. Artificial Bee Colony Optimization (2/8)
- 7. Artificial Bee Colony Optimization (3/8)
- 7. Artificial Bee Colony Optimization (4/8)
- 7. Artificial Bee Colony Optimization (5/8)
- 7. Artificial Bee Colony Optimization (6/8)
- 7. Artificial Bee Colony Optimization (7/8)
- 7. Artificial Bee Colony Optimization (8/8)
- 8. Wolf-Based Search Algorithms (1/3)
- 8. Wolf-Based Search Algorithms (2/3)
- 8. Wolf-Based Search Algorithms (3/3)
- 9. Bird's-Eye View (1/3)
- 9. Bird's-Eye View (2/3)
- 9. Bird's-Eye View (3/3)
- Back Cover
Product information
- Title: Swarm Intelligence
- Author(s):
- Release date: September 2018
- Publisher(s): CRC Press
- ISBN: 9781498741071
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