- 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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