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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.
Introduction
Sources of Inspiration
Random Variables
Pseudo-Random Number Generation
Random Walk
Chaos
Chapter Conclusion
Bibliography
Bat Algorithm
Bat Algorithm (BA)
BA Variants
Bat Hybridizations
BA in Real-World Applications
Chapter Conclusion
Bibliography
Artificial Fish Swarm Algorithm
Fish Swarm Optimization
Artificial Fish Swarm Algorithm (AFSA) Variants
AFSA Hybridizations
Fish Swarm in Real-World Applications
Chapter Conclusion
Bibliography
Cuckoo Search Algorithm
Cuckoo Search (CS)
CS Variants
CS Hybridizations
CS in Real-World Applications
Chapter Conclusion
Bibliography
Firefly Algorithm
Firefly Algorithm (FFA)
FFA Variant
FFA Hybridizations
Firefly in Real-World Applications
Chapter Conclusion
Bibliography
Flower Pollination Algorithm
Flower Pollination Algorithm (FPA)
FPA Variants
FPA: Hybridizations
Real-World Applications of the FPA
FPA in Feature Selection
Chapter Conclusion
Bibliography
Artificial Bee Colony Optimization
Artificial Bee Colony (ABC)
ABC Variants
ABC Hybridizations
ABC in Real-World Applications
Chapter Conclusion
Bibliography
Wolf-Based Search Algorithms
Wolf Search Algorithm (WSA)
Wolf Search Optimizers in Real-World Applications
Chapter Conclusion
Bibliography
Bird´s-Eye View
Criteria (1) Classification According to Swarm Guide
Criteria (2) Classification According to the Probability Distribution Used
Criteria (3) Classification According to the Number of Behaviors Used
Criteria (4) Classification According to Exploitation of Positional Distribution of Agents
Criteria (5) Number of Control Parameters
Criteria (6) Classification According to Either Generation of Completely New Agents per Iteration
Criteria (7) Classification Based on Exploitation of Velocity Concept in the Optimization
Criteria (8) Classification According to the Type of Exploration/Exploitation Used
Chapter Conclusion