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.
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