Springer Socio-cultural Inspired Metaheuristics: 828 (Studies in Computational Intelligence, 828)

This book presents the latest insights and developments in the field of socio-cultural inspired algorithms. Akin to evolutionary and swarm-based optimization algorithms, socio-cultural algorithms belong to the category of metaheuristics (problem-independent computational methods) and are inspired by natural and social tendencies observed in humans by which they learn from one another through social interactions. This book is an interesting read for engineers, scientists, and students studying/working in the optimization, evolutionary computation, artificial intelligence (AI) and computational intelligence fields. From the Back Cover This book presents the latest insights and developments in the field of socio-cultural inspired algorithms. Akin to evolutionary and swarm-based optimization algorithms, socio-cultural algorithms belong to the category of metaheuristics (problem-independent computational methods) and are inspired by natural and social tendencies observed in humans by which they learn from one another through social interactions. This book is an interesting read for engineers, scientists, and students studying/working in the optimization, evolutionary computation, artificial intelligence (AI) and computational intelligence fields. About the Author Anand J. Kulkarni holds a Ph.D. in Distributed Optimization from Nanyang Technological University, Singapore, M.S. in AI from the University of Regina, Canada, and Bachelor of Engineering from Shivaji University, India. He worked as a Research Fellow on a cross-border supply-chain disruption project at Odette School of Business, University of Windsor, Canada. Currently, he is working as Head and Associate Professor at the Symbiosis Institute of Technology, Pune, India. His research interests include optimization algorithms, multi-objective optimization, multi-agent systems, complex systems, swarm optimization, game theory and self-organizing systems. He is the founder and chairman of the OAT Research Lab. Anand has published over 40 research papers in peer-reviewed journals and conferences along with two books. Pramod Kumar Singh is an Associate Professor in Information and Communication Technology at the ABV-Indian Institute of Information Technology and Management, Gwalior, India. He completed his B. Tech. from the Kamla Nehru Institute of Technology, Sultanpur, UP, India, and M. Tech. and Ph.D. from the Indian Institute of Technology, Kharagpur, India. His research interest includes computational intelligence, multi-objective optimization, data mining, text analytics, and machine learning. He is member of IEEE and ACM. He has been Associate Editor of IJSI and Guest Editor of JOCS. Besides, he is on the reviewer board of various reputed journals published by IEEE (including Transactions), Elsevier, Springer, ACM, and Taylor and Francis. He has published more than 70 research papers in various international journals and conferences. Suresh Chandra Satapathy is a Professor at the School of Computer Engineering, KIIT, Odisha, India. Previously, he was a Professor and Head of the Department of CSE at ANITS, AP, India. He received his Ph.D. in CSE from JNTU, Hyderabad and M.Tech. in CSE from the NIT, Odisha. He has more than 27 years of teaching and research experience. His research interests include machine learning, data mining, swarm intelligence and applications. He has published more than 98 papers in respected journals and conferences and has edited numerous volumes for Springer AISC and LNCS. In addition to serving on the editorial board of several journals, he is a senior member of the IEEE and a life member of the Computer Society of India, where he is the National Chairman of Division-V (Education and Research). Ali Husseinzadeh Kashan completed his Ph.D. and postdoc in Industrial Engineering at Amirkabir University of Technology, Iran. Currently, he is an Associate Professor at the Department of ISE, Tarbiat M