Articles liés à Non-Convex Multi-Objective Optimization

Non-Convex Multi-Objective Optimization - Couverture rigide

Pardalos, Panos M.; ilinskas, Antanas; ilinskas, Julius

 
9783319610054: Non-Convex Multi-Objective Optimization
Couverture rigide
ISBN 10 : 3319610058 ISBN 13 : 9783319610054
Editeur : Springer, 2017

Synopsis

Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in chemical engineering, and business process management are included to aide researchers and graduate students in mathematics, computer science, engineering, economics, and business management.  

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l'auteur

Atefeh Hemmati received her B.S. degree in Computer Engineering, Information Technology from Central Tehran Branch, IAU, Tehran, Iran in 2020 and received her M.S. degree in Computer Engineering, Software from the Science and Research Branch, IAU, Tehran, Iran in 2023. Her research interests include Internet of Things, LLMs, fog/cloud/edge computing, and artificial intelligence, especially in the fields of machine learning, deep learning. She has authored several publications in these fields
and actively contributes to advanced AI applications in IoT ecosystems.
Amir Masoud Rahmani received his B.S. in computer engineering from Amir Kabir University, Tehran, in 1996, his M.S. in computer engineering from Sharif University of Technology, Tehran, in 1998, and his Ph.D. in computer engineering from IAU University, Tehran, in 2005. Currently, he is a professor of computer engineering. His research interests include Machine Learning, the Internet of Things, cloud/fog computing, and artificial intelligence.
Fatemeh Bazikar received her B.S. degree in Applied Mathematics, Shahid Chamran University, Ahvaz, Iran, in 2011, her M.S. in Applied Mathematics (Optimization), Shahid Chamran University, Ahvaz, Iran, in 2013, her Ph.D. in Applied Mathematics, Guilan University, Rasht, Iran, in 2021, and her Postdoc Researcher in Department of Computer Science, Faculty of Mathematical Sciences, Alzahra University, Tehran, Iran, in 2024. Her research interests include Machine Learning, Optimization, Data Analysis, Mathematical Programming, and artificial intelligence, especially in the fields of machine learning.
Hossein Moosaei

À propos de la quatrième de couverture

Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in chemical engineering, and business process management are included to aide researchers and graduate students in mathematics, computer science, engineering, economics, and business management. 

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Springer
Date d'édition
2017
Langue
anglais
ISBN 10 
3319610058
ISBN 13 
9783319610054
Reliure
Relié
Numéro d'édition
1
Nombre de pages
192
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