Grey Wolf Optimizer

A Pack of Solutions for Your Optimization Problems
Erscheinungsjahr 2026
ISBN: 978-0-443-36624-6
Verlag: Elsevier Science

Buch, Englisch, 250 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

Grey Wolf Optimizer: A Pack of Solutions for Your Optimization Problems offers in-depth coverage of recent theoretical advancements in GWO, as well as several variants, improvements, and hybrid approaches developed to enhance the GWO's performance and adaptability. The use of generative AI to improve this algorithm and make it more generic is also explored, along with diverse applications across multiple fields to illustrate the practical utility and versatility of the methods presented. The GWO algorithm is an influential and rapidly advancing metaheuristic algorithm that has gained substantial attention across scientific and industrial domains. However, solving optimization problems using the GWO involves addressing various challenges, including but not limited to: handling multiple objectives, managing constraints, working with binary decision variables, navigating large-scale search spaces, adapting to dynamic objective functions, and dealing with noisy or uncertain parameters. This book directly addresses these needs by providing a thorough exploration of the GWO, offering a deep dive into the algorithm's foundations and presenting new developments to help researchers overcome common challenges. The book features numerous case studies and real-world examples across various fields, such as engineering, healthcare, finance, and environmental management. These applications demonstrate the versatility and effectiveness of the GWO in addressing complex, interdisciplinary challenges, making the content highly relevant and practical for readers. Written by some of the world’s most highly cited researchers in the field of artificial intelligence, algorithms, and machine learning, the book serves as an essential resource for researchers and practitioners interested in applying and developing the Grey Wolf Optimizer.

Autoren/Hrsg.

Mirjalili, Seyedali

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware

Inhaltsverzeichnis

1. Introduction to meta-heuristics and Grey Wolf Optimizer
2. Single-objective optimization using Grey Wolf Optimizer
3. Multi-objective optimization using Grey Wolf Optimizer
4. Many-objective optimization using Grey Wolf Optimizer
5. Constrained optimization using Grey Wolf Optimizer
6. Robust optimization using Grey Wolf Optimizer
7. Binary optimization using Grey Wolf Optimizer
8. Dynamic Optimization using Grey Wolf Optimizer
9. Combinatorial optimization using Grey Wolf Optimizer
10. Oppositional-based learning with Grey Wolf Optimizer
11. Chaotic Grey Wolf Optimizer
12. Memetic Grey Wolf Optimizer
13. Feature selection using Grey Wolf Optimizer
14. Clustering using Grey Wolf Optimizer
15. Hybrids of Grey Wolf Optimizer with swarm intelligence methods
16. Hybrids of Grey Wolf Optimizer with evolutionary algorithms
17. Engineering optimization using Grey Wolf Optimizer
18. Grey Wolf Optimizer with Genetic Programming
19. Grey Wolf Optimizer in sustainable energy
20. Grey Wolf Optimizer in network and 5G

Über Autor(innen)

Mirjalili, Seyedali
Dr. Seyedali Mirjalili is a Professor and globally renowned leader in artificial intelligence and
optimization, recognized as the No. 1 AI researcher on Stanford University's prestigious World's Top Scientists list since 2023. He founded the Centre for Artificial Intelligence Research and
Optimization in 2019 and serves as a Professor of AI at Torrens University Australia, with distinguished professorships in Hungary and the Czech Republic. With more than 600 research
publications, 130,000 citations, and an H-index of 125, Prof. Mirjalili is among the top 1% of highly cited researchers worldwide. His contributions include developing AI algorithms widely applied in science and industry and delivering influential talks, including a TED Talk on AI's transformative potential. Prof. Mirjalili is a strong advocate for responsible and inclusive AI, and he has collaborated with industry and government on ethical AI tools. As a senior member of IEEE and an editor for leading AI journals, he significantly contributed to the advancements of fundamental and applied research in the field. Recognized as a top research leader by The
Australian for five years, his insights have earned significant media attention, which showcases
his influence as a global thought leader.