Sine Cosine Algorithm for Optimization (SpringerBriefs in Computational Intelligence) - Tapa blanda

Dr. Jagdish Chand Bansal is an Associate Professor at South Asian University New Delhi and Visiting Faculty at Maths and Computer Science, Liverpool Hope University UK. Dr. Bansal obtained his Ph.D. in Mathematics from IIT Roorkee. Before joining SAU New Delhi he worked as an Assistant Professor at ABV- Indian Institute of Information Technology and Management Gwalior and BITS Pilani. His Primary area of interest is Swarm Intelligence and Nature Inspired Optimization Techniques. Recently, he proposed a fission-fusion social structure based optimization algorithm, Spider Monkey Optimization (SMO), which is being applied to various problems in the engineering domain. He has published more than 70 research papers in various international journals/conferences. He is the Section Editor (Mathematics and Statistics) of the journal MethodsX published by Elsevier. He is the series editor of the book series Algorithms for Intelligent Systems (AIS), Studies in Autonomic, Data-driven and Industrial Computing (SADIC), and Innovations in Sustainable Technologies and Computing (ISTC) published by Springer. He is also the Associate Editor of Engineering Applications of Artificial Intelligence (EAAI) and ARRAY published by Elsevier. He is the general secretary of the Soft Computing Research Society (SCRS). He has also received Gold Medal at UG and PG levels.

 

Prathu Bajpai is currently pursuing a Ph.D. in Evolutionary and Swarm Algorithms in the faculty of Mathematics and Computer Science at South Asian University. He obtained his master's degree in Mathematics from IIT Gandhinagar India. Nature-inspired algorithms is his major research area.

 

Anjali Rawat completed her bachelor’s degree from Gargi College, University of Delhi. She obtained her master's degree from South Asian University. Currently, she is pursuing her Ph.D. at the National Institute of Technology, Mizoram. Optimization using meta-heuristics is her major research area.

 

Atulya K. Nagar holds the Foundation Chair as Professor of Mathematical Sciences and currently he is the Pro-Vice-Chancellor for Research at Liverpool Hope University, United Kingdom. He has been the Founding Dean of the Faculty of Science, and Founding Head of the School of Mathematics, Computer Science and Engineering which he established at the University. He received a prestigious Commonwealth Fellowship for pursuing his doctorate (DPhil) in Applied Nonlinear Mathematics, which he earned from the University of York (UK) in 1996. He holds BSc (Hons), MSc (Mathematics), and MPhil (with distinction) in Mathematics from the MDS University of Ajmer, India. Prior to joining Liverpool Hope, he was with the Department of Mathematical Sciences, and later at the Department of Systems Engineering, at Brunel University, London. He is an internationally respected scholar working at the cutting edge of theoretical computer science, applied mathematical analysis, and systems engineering with his research expertise spanning both applied mathematics and computational methods for nonlinear, complex, and intractable problems arising in science, engineering, and industry. He has edited volumes on Intelligent Systems, and Applied Mathematics. He has been the Editor-in-Chief of the International Journal of Artificial Intelligence and Soft Computing (IJAISC) and serves on editorial boards for a number of prestigious journals; and is one of the co-editors for the Springer Book series on Algorithms for Intelligent Systems (AIS); and for another one on Innovations in Sustainable Technologies and Computing (ISTC). He is well published with over 500 publications in prestigious publishing outlets. Prof Nagar sits on a number of strategic UK wide research bodies  such as the JISC Research Strategy group and he is the fellow of the Institute of Mathematics and its Applications (FIMA); and fellow of the Higher Education Academy (FHEA).

This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA.