Descrição
In the realm of optimization techniques, there exists a powerful method known as particle swarm optimization (PSO), which is elaborately explored by Jun Sun. This work delves into both classical and quantum perspectives, providing readers with a comprehensive understanding of the PSO algorithm. It is particularly commendable for its straightforward parameter requirements, making it accessible for both novices and seasoned practitioners.
The author meticulously breaks down the mechanics of PSO, illustrating how it mimics social behavior patterns observed in nature. Sun adeptly addresses the strengths and limitations of the algorithm, while also incorporating quantum theories to enhance the standard approach, presenting readers with innovative insights and applications.
This book serves as a valuable resource for researchers and engineers alike, bridging the gap between theoretical concepts and practical implementations. With a balanced mix of theory and real-world examples, it invites readers to explore the intricate world of optimization, emphasizing the transformative potential of PSO in tackling complex problems across diverse disciplines.
The author meticulously breaks down the mechanics of PSO, illustrating how it mimics social behavior patterns observed in nature. Sun adeptly addresses the strengths and limitations of the algorithm, while also incorporating quantum theories to enhance the standard approach, presenting readers with innovative insights and applications.
This book serves as a valuable resource for researchers and engineers alike, bridging the gap between theoretical concepts and practical implementations. With a balanced mix of theory and real-world examples, it invites readers to explore the intricate world of optimization, emphasizing the transformative potential of PSO in tackling complex problems across diverse disciplines.
Detalhes do Livro
Formato
eBook
Páginas
419 páginas
Idioma
Inglês
Publicado
mai 10, 2014
Editora
CRC Press
Edições
3 edições
ISBN-10
1280121920
ISBN-13
9781280121920