Overview
- Rich contributions from leading experts worldwide
- A complete state-of-the-art reference of the formal optimization methods and applications
- Includes critical literature review, optimization method description & mathematical formulation, flowcharts, etc
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About this book
The handbook on formal optimization discusses background/literature review, optimization method description including mathematical formulation, flowcharts/pseudocodes, illustrations, problems and application(s), results and critical discussions, flowcharts/pseudocodes, etc. The editors have brought together almost every aspect of this enormous field of formal optimization such as mathematical and Bayesian optimization, neural networks and deep learning, genetic algorithms and applications, hybrid optimization methods, combinatorial optimization, constraint handling in optimization methods, swarm-based optimization, among others. The handbook serves as a complete reference discussing a wide aspect of formal optimization methods. This handbook will be useful for experts as well as non-specialists as they will find the material stimulating. The book covers research trends, challenges, and prospective topics as well.
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Keywords
- Engineering Optimization
- Nature inspired Methods
- Heuristics
- Resource Optimization
- Machine Learning
- Neural Networks and Deep Learning
- Optimization in Manufacturing Processes
- Swarm-based Optimization
- Physics-based Optimization
- Bio-inspired Optimization
- Evolutionary Optimization-based Methods
- Bayesian Optimization
- Mathematical Optimization/Programming
- Constraint Handling in Optimization Methods
Table of contents (55 entries)
Editors and Affiliations
About the editors
Anand J Kulkarni holds a PhD in Artificial Intelligence (AI) based Distributed Optimization from Nanyang Technological University, Singapore, MS in AI from the University of Regina, Canada. He worked as a Postdoctoral Research Fellow at Odette School of Business, University of Windsor, Canada. Anand has a Bachelor of Engineering in Mechanical Engineering from the Shivaji University, India, and holds a Diploma from the Board of Technical Education, Mumbai, India. Since 2021, he has been working as a Research Professor and Associate Director of the Institute of Artificial Intelligence at the MITWPU, Pune, India. His research interests include AI-based nature-inspired optimization algorithms and self-organizing systems. Anand pioneered optimization methodologies such as Cohort Intelligence, Ideology Algorithm, Expectation Algorithm, Socio Evolution & Learning Optimization Algorithm, Leader-Advocate-Believer Algorithm, and Snail Homing and Mating Search Algorithm. Anand has published over 80 research papers in peer-reviewed reputed journals, chapters, and conferences along with 7 authored and 15 edited books. He has so far guided 6 doctoral, 10 masters, and over 100 UG students. Anand is the lead series editor for Springer and Taylor & Francis as well as associate editor of Elsevier journals such as ‘Engineering Applications of Artificial Intelligence’ and ‘Systems and Soft Computing’ as well as IOS Press KES journal. He is the recipient of the best paper award in IEEE ICNSC, Chicago, USA, and 'The Swatantry Veer Savarkar Award' 2023 by ‘Pune Marathi Granthalay’, Pune for his Marathi book entitled 'Artificial Intelligencechya Watewar'.
Amir H. Gandomi is a Professor of Data Science and an ARC DECRA Fellow at the Faculty of Engineering & Information Technology, University of Technology Sydney. Before joining UTS, Prof. Gandomi was an Assistant Professor at the Stevens Institute of Technology and a distinguished research fellow at BEACON Center, Michigan State University. Prof. Gandomi has published 400+ journal papers and 14 books. He has received multiple prestigious awards for his research excellence and impact, such as the 2023 Achenbach Medal and the 2022 Walter L. Huber Prize, the highest-level mid-career research award in all areas of civil engineering. He has served as associate editor, editor, and guest editor in several prestigious journals. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are data analytics and global optimization (big) in real-world problems in particular.
Bibliographic Information
Book Title: Handbook of Formal Optimization
Editors: Anand J. Kulkarni, Amir H. Gandomi
DOI: https://doi.org/10.1007/978-981-19-8851-6
Publisher: Springer Singapore
eBook Packages: Springer Reference Intelligent Technologies and Robotics, Reference Module Computer Science and Engineering
eBook ISBN: 978-981-19-8851-6Due: 02 June 2024
Number of Pages: XX, 1080
Number of Illustrations: 40 b/w illustrations, 109 illustrations in colour
Topics: Computational Intelligence, Optimization, Artificial Intelligence, Operations Research/Decision Theory