Handbook of Whale Optimization Algorithm: Variants, Hybrids, Improvements, and Applications - Tapa blanda

Acerca del autor

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.

De la contraportada

Metaheuristics are general-purpose problem-solving Artificial Intelligence techniques that can be used to solve any sort of optimization problems subject to proper configuration. Handbook of Whale Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides the most in-depth look at an emerging meta-heuristic that has been widely used in both science and industry.

Whale Optimization Algorithm has been cited more than 5000 times in Google Scholar. Solving optimization problems using this algorithm requires addressing a number of challenges including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters, just to name a few. This handbook provides readers with in-depth analysis.

of this algorithm and the existing methods in the literature to cope with such challenges. The authors and editors also propose several improvements, variants, and hybrids of this algorithm. Several applications are also covered to demonstrate the applicability of methods in this book.