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 ^(R) 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.