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SWARM INTELLIGENCE. PRINCIPLES, ADVANCES, AND APPLICATIONS
Título:
SWARM INTELLIGENCE. PRINCIPLES, ADVANCES, AND APPLICATIONS
Subtítulo:
Autor:
HASSANIEN, A
Editorial:
CRC PRESS
Año de edición:
2015
Materia
INTELIGENCIA ARTIFICIAL - GENERAL
ISBN:
978-1-4987-4106-4
Páginas:
210
99,50 €

 

Sinopsis

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