Advanced Concepts in Grey Wolf Optimizer: Leading the Pack in Advanced Optimization

Seyedali Mirjalili

Dec 2026 · Morgan Kaufmann

Ebook

300

Pages

Eligible

This book will become available on December 1, 2026. You will not be charged until it is released.

About this ebook

Advanced Concepts in Grey Wolf Optimizer: Leading the Pack in Advanced Optimization provides in-depth coverage of recent theoretical advancements in GWO, as well as advanced methods to handle issues such as multiple objectives, constraints, binary variables, large search spaces, dynamic goals, and uncertain data. This book assumes familiarity with optimization fundamentals and therefore dives directly into multi-objective, constrained, binary, and dynamic-environment variants, as well as GWO-ML/LLM hybrids. Extensive real-world case studies in areas such as energy systems, supply-chain design, LLM fine-tuning, robotics, and finance ensure that both scholars and engineers can translate the material into deployable solutions. The authors present important new theories, hybrids with Machine Learning/Deep Learning, and hybrid methods that increase GWO's performance. 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. 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 advanced resource for researchers and practitioners interested in applying and developing the Grey Wolf Optimizer. - Presents the use of new AI tools such as Generative AI (GenAI), Large Language Models (LLM), and Data Processing (DP) with the Grey Wolf Optimizer, showing readers how these technologies can improve and expand GWO capabilities - Provides a comprehensive overview of the latest GWO modifications and hybrid approaches, including methods to handle complex challenges such as multi-objective tasks, constraints, noisy data, and dynamic conditions - Includes many practical examples and real-world case studies from areas such as engineering, healthcare, finance, and robotics

Discover more

About the author

Dr. Seyedali Mirjalili is a Professor and globally renowned leader in artificial intelligence andoptimization, 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 andOptimization 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 researchpublications, 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 TheAustralian for five years, his insights have earned significant media attention, which showcaseshis influence as a global thought leader.

Reading information

Smartphones and tablets

Install the Google Play Books app for Android and iPad/iPhone. It syncs automatically with your account and allows you to read online or offline wherever you are.

Laptops and computers

You can listen to audiobooks purchased on Google Play using your computer's web browser.

eReaders and other devices

To read on e-ink devices like Kobo eReaders, you'll need to download a file and transfer it to your device. Follow the detailed Help Center instructions to transfer the files to supported eReaders.

Flag as inappropriate