Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm
Foundations, Methods, and Applications
Published on 7. November 2025
Book
Paperback/Softback
348 pages
978-0-443-33775-8 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Philadelphia
United States
Target group
Professional and scholarly
Dimensions
Height: 229 mm
Width: 152 mm
Weight
560 gr
ISBN-13
978-0-443-33775-8 (9780443337758)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Mohd Herwan Sulaiman | Zuriani Mustaffa
Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm
Foundations, Methods, and Applications
E-Book
11/2025
Elsevier
€181.99
Available for download
Persons
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
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
Content
Part I: Modern Energy System Challenges: Fundamental Methodologies, Opportunities, and Solutions
1. Challenges of renewable energy systems and the artificial intelligence opportunity
2. Fundamentals of swarm-based algorithms
3. Fundamentals of evolution-based algorithms
4. Fundamentals of physics- and human-based algorithms
5. Fundamentals of the Barnacles Mating Optimizer
6. The Evolutionary Mating Algorithm: principles and applications
7. Deep learning approaches
7.i. Supervised learning with feedforward neural networks (FFNN)
7.ii. Other deep learning families
Part II: Applications for Renewable Energy Systems
8. State of charge (SOC) estimation in electric vehicles using deep learning feedforward neural networks
9. Hybrid of metaheuristic learning with deep learning in battery management of electric vehicles
10. Optimal reactive power dispatch using the Barnacle Mating Optimizer
11. Optimal power flow solutions enhanced by the Evolutionary Mating Algorithm
12. Renewable energy power forecasting, enhanced by hybrid Barnacle Mating Optimizer-Evolutionary Mating Algorithm deep learning
12.i. Solar power
12.ii. Wind power
1. Challenges of renewable energy systems and the artificial intelligence opportunity
2. Fundamentals of swarm-based algorithms
3. Fundamentals of evolution-based algorithms
4. Fundamentals of physics- and human-based algorithms
5. Fundamentals of the Barnacles Mating Optimizer
6. The Evolutionary Mating Algorithm: principles and applications
7. Deep learning approaches
7.i. Supervised learning with feedforward neural networks (FFNN)
7.ii. Other deep learning families
Part II: Applications for Renewable Energy Systems
8. State of charge (SOC) estimation in electric vehicles using deep learning feedforward neural networks
9. Hybrid of metaheuristic learning with deep learning in battery management of electric vehicles
10. Optimal reactive power dispatch using the Barnacle Mating Optimizer
11. Optimal power flow solutions enhanced by the Evolutionary Mating Algorithm
12. Renewable energy power forecasting, enhanced by hybrid Barnacle Mating Optimizer-Evolutionary Mating Algorithm deep learning
12.i. Solar power
12.ii. Wind power