Constraint Handling in Metaheuristics and Applications

Name: Constraint Handling in Metaheuristics and Applications
SKU: 978-981-3367-12-8
Price: 198.00 CHF
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Artikelnummer: 978-981-3367-12-8

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This book aims to discuss the core and underlying principles and analysis of the different constraint handling approaches. The main emphasis of the book is on providing an enriched literature on mathematical modelling of the test as well as real-world problems with constraints, and further development of generalized constraint handling techniques. These techniques may be incorporated in suitable metaheuristics providing a solid optimized solution to the problems and applications being addressed. The book comprises original contributions with an aim to develop and discuss generalized constraint handling approaches/techniques for the metaheuristics and/or the applications being addressed. A variety of novel as well as modified and hybridized techniques have been discussed in the book. The conceptual as well as the mathematical level in all the chapters is well within the grasp of the scientists as well as the undergraduate and graduate students from the engineering and computer science streams. The reader is encouraged to have basic knowledge of probability and mathematical analysis and optimization. The book also provides critical review of the contemporary constraint handling approaches. The contributions of the book may further help to explore new avenues leading towards multidisciplinary research discussions. This book is a complete reference for engineers, scientists, and students studying/working in the optimization, artificial intelligence (AI), or computational intelligence arena.

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Verlag	Springer
Einband	Kartonierter Einband (Kt)
Erscheinungsjahr	2022
Seitenangabe	348 S.
Ausgabekennzeichen	Englisch
Masse	H23.5 cm x B15.5 cm x D1.9 cm 528 g
Autor	Kulkarni, Anand J. (Hrsg.) / Mezura-Montes, Efrén (Hrsg.) / Wang, Yong (Hrsg.) / Gandomi, Amir H. (Hrsg.) / Krishnasamy, Ganesh (Hrsg.)

Über den Autor Anand J. (Hrsg.) Kulkarni

Anand J Kulkarni holds a PhD in Artificial Intelligence (AI) based Distributed Optimization from Nanyang Technological University, Singapore, MS in AI from University of Regina, Canada. He worked as Postdoctoral Research Fellow at Odette School of Business, University of Windsor, Canada. Since 2021, he is working as 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 based algorithm and snail homing & mating search algorithm. Dr. Anand has published over 120 research papers in peer-reviewed reputed journals, chapters and conferences along with 7 authored and 15 edited books. He has guided 6 doctoral, 10 master's and over 60 UG students. Dr. Anand is the lead series editor for several reputed publishers. He is the recipient of the best paper award in IEEE ICNSC, Chicago, USA and 'Swatantryveer Savarakar Award' 2023 by 'Pune Marathi Granthalay', Pune for his Marathi book entitled 'Artificial Intelligencechya Watewar'. Apoorva S Shastri holds a PhD in Optimization Algorithms and Applications from Symbiosis International (Deemed University), Master of Technology (M.Tech) in VLSI Design and Bachelor of Engineering in Electronics & Product Design Technology from R.T.M.N.U, Nagpur. She has also done a Diploma from the Govt. Polytechnic, Nagpur. She worked as a guest faculty at Centre for Development of Advanced Computing (C-DAC), Pune. Currently, she is a Research Assistant Professor at Institute of Artificial Intelligence at the MITWPU, Pune, India. Her research interests include optimization algorithms, VLSI design, multi-objective optimization, continuous, discrete, and combinatorial optimization, complex systems, manufacturing, and self-organizing systems. Dr. Apoorva developed socio-inspired optimization methodologies such as multi-cohort intelligence algorithm, expectation algorithm and LAB algorithm, and snail homing and mating search algorithm. Dr. Apoorva has published several research papers in peer reviewed journals, chapters, and conferences along with 1 authored and 5 edited books. Hossein Bonakdari is an Associate Professor of Civil Engineering at the University of Ottawa, specializing in AI-driven hydrology, hydraulics, wildfire/flood modeling, and climate resilience. He has published over 310 papers and authored four books. Dr. Bonakdari develops advanced algorithms to extract insights from big datasets. His innovative work addresses pressing environmental challenges, including floods, droughts, and wildfires, while making significant contributions to both national and international research communities.