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Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm: Foundations, Methods, and Applications (Advances in Intelligent Energy Systems) - Softcover

Sulaiman, Mohd Herwan; Mustaffa, Zuriani

 
9780443337758: Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm: Foundations, Methods, and Applications (Advances in Intelligent Energy Systems)
Softcover
ISBN 10: 0443337756 ISBN 13: 9780443337758
Publisher: Elsevier, 2025

Synopsis

Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm: Foundations, Methods, and Applications reveals the potential of innovative optimization algorithms to support sustainability in modern energy systems. This book provides a multidisciplinary foundation for the reader, with Part I breaking down fundamentals including the challenges to be addressed in renewable energy systems and detailed methodologies including swarm-, physics-, and human-based algorithms, before introducing the Barnacles Mating Optimizer and Evolutionary Mating Algorithm themselves. Part II drills deeper into examples, case studies, and applications for energy systems, offering comparative analysis with alternative tools, and providing complimentary MATLAB code using the latest Toolbox. A sandbox for readers to learn, skill-build, and develop in, ‘Intelligent Energy Systems using BMO and EMA’ provides an indispensable guide to these cutting-edge AI tools for new and experienced readers.

  • Builds step-by-step from foundational principles to complex applications in sustainable energy systems
  • Includes case studies, tools, and complimentary MATLAB code to try out, rework, and apply to new problems
  • Guides readers through these innovative methods, as part of the ground-breaking Advances in Intelligent Energy Systems

"synopsis" may belong to another edition of this title.

About the Authors

Mohamed Herwan Sulaiman currently serves as an Associate Professor in the Faculty of Electrical and Electronics Engineering Technology at the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Malaysia. His research interests lie in power system optimization and swarm intelligence applications to power system studies. He has authored and co-authored more than 150 technical papers in the international journals and conferences and has been invited as a Journal reviewer for several international impact journals in the field of power systems and soft computing applications and many more.



Zuriani Mustaffa is a Senior Lecturer in the Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Malaysia. She holds a PhD in Computer Science from the Universiti Utara Malaysia. Her research interests include Computational Intelligence (CI) algorithm and machine learning techniques. Her research area focuses on hybrid algorithms which involves optimization and machine learning techniques with particular attention for time series predictive analysis

From the Back Cover

Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm: Foundations, Methods, and Applications reveals the potential of innovative optimization algorithms to support sustainability in modern energy systems. This book provides a multidisciplinary foundation for the reader, with Part I breaking down fundamentals including the challenges to be addressed in renewable energy systems and detailed methodologies including swarm-, physics-, and human-based algorithms, before introducing the Barnacles Mating Optimizer and Evolutionary Mating Algorithm themselves. Part II drills deeper into examples, case studies, and applications for energy systems, offering comparative analysis with alternative tools, and providing complimentary MATLAB code using the latest Toolbox. A sandbox for readers to learn, skill-build, and develop in, ‘Intelligent Energy Systems using BMO and EMA’ provides an indispensable guide to these cutting-edge AI tools for new and experienced readers.

"About this title" may belong to another edition of this title.

Publisher
Elsevier
Publication date
2025
Language
English
ISBN 10 
0443337756
ISBN 13 
9780443337758
Binding
Paperback
Edition number
1
Number of pages
348