Articoli correlati a Intelligent Energy Systems using the Barnacles Mating...

Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm: Foundations, Methods and Applications - Brossura

Sulaiman

 
9780443337758: Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm: Foundations, Methods and Applications
Brossura
ISBN 10: 0443337756 ISBN 13: 9780443337758
Casa editrice: Elsevier, 2025

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sugli autori

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

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Elsevier
Data di pubblicazione
2025
Lingua
Inglese
ISBN 10 
0443337756
ISBN 13 
9780443337758
Rilegatura
Copertina flessibile
Numero edizione
1
Numero di pagine
348
Contatto del produttore
non disponibile
Persona responsabile
GPSR Kontakt
gpsr@libri.de

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