The formal optimization handbook is a comprehensive guide that covers a wide range of subjects. It includes a literature review, a mathematical formulation of optimization methods, flowcharts and pseudocodes, illustrations, problems and applications, results and critical discussions, and much more. The book covers a vast array of formal optimization fields, including mathematical and Bayesian optimization, neural networks and deep learning, genetic algorithms and their applications, hybrid optimization methods, combinatorial optimization, constraint handling in optimization methods, and swarm-based optimization. This handbook is an excellent reference for experts and non-specialists alike, as it provides stimulating material. The book also covers research trends, challenges, and prospective topics, making it a valuable resource for those looking to expand their knowledge in this field.
Author Biography:
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.
