Swarm Intelligence

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Opis: Swarm Intelligence - Eid Emary, Aboul-Ella Hassanien

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.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


Szczegóły: Swarm Intelligence - Eid Emary, Aboul-Ella Hassanien

Tytuł: Swarm Intelligence
Autor: Eid Emary, Aboul-Ella Hassanien
Producent: Productivity Press Inc
ISBN: 9781498741064
Rok produkcji: 2015
Ilość stron: 228
Oprawa: Twarda


Recenzje: Swarm Intelligence - Eid Emary, Aboul-Ella Hassanien

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Swarm Intelligence

,

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.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

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