Swarm Intelligence: Principles, Advances, and Applications

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

Chapter 1: Introduction
Chapter 2: Bat Algorithm (Ba)
Chapter 3: Artificial Fish Swarm
Chapter 4: Cuckoo Search Algorithm
Chapter 5: Firefly Algorithm (Ffa)
Chapter 6: Flower Pollination Algorithm
Chapter 7: Artificial Bee Colony Optimization
Chapter 8: Wolf-Based Search Algorithms
Chapter 9: Bird’S-Eye View