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
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.