Soft Computing for Problem Solving: Socpros 2018, Volume 1

Das, Kedar Nath, Bansal, Jagdish Chand, Deep, Kusum

  • 出版商: Springer
  • 出版日期: 2019-11-28
  • 售價: $2,570
  • 貴賓價: 9.5$2,442
  • 語言: 英文
  • 頁數: 1013
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9811500347
  • ISBN-13: 9789811500343
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This two-volume book presents the outcomes of the 8th International Conference on Soft Computing for Problem Solving, SocProS 2018. This conference was a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), and Vellore Institute of Technology (India), and brought together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions. The book highlights the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers on algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It offers a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems that are difficult to solve using traditional methods.


Dr. Kedar Nath Das is an Assistant Professor at the Department of Mathematics, National Institute of Technology, Silchar, Assam, India. Over the past 10 years, he has made substantial contributions to research on soft computing, and has published several research papers in prominent national and international journals. His chief area of interest is in evolutionary and bio-inspired algorithms for optimization.

Dr. Jagdish Chand Bansal is an Associate Professor at the South Asian University, New Delhi, India and visiting research fellow at Liverpool Hope University, Liverpool, UK. He has an excellent academic record and is a leading researcher in the field of swarm intelligence. Further, he has published numerous research papers in respected international and national journals.

Prof. Kusum Deep is a Professor at the Department of Mathematics, Indian Institute of Technology Roorkee, India. Over the past 25 years, her research has made her a central international figure in the areas of nature-inspired optimization techniques, genetic algorithms and particle swarm optimization.

Prof. Atulya Nagar holds the Foundation Chair as Professor of Mathematical Sciences and is Dean of the Faculty of Science at Liverpool Hope University, UK. Prof. Nagar is an internationally respected scholar working at the cutting edge of theoretical computer science, applied mathematical analysis, operations research, and systems engineering. He received a prestigious Commonwealth Fellowship for pursuing his doctorate (DPhil) in Applied Non-Linear Mathematics, which he earned from the University of York (UK) in 1996; and he holds BSc (Hons.), MSc, and MPhil (with Distinction) from the MDS University of Ajmer, India.

Prof. P. Ponnambalam is an Associate Professor at the School of Electrical Engineering, VIT University, India. His areas of research interests are Multilevel Converters, Fuzzy controller for multilevel converters, MPC controllers, Thermoelectric Generators for Solar Photo voltaic cells areas in which he is actively publishing. He is having 15 years of teaching experience.

Prof. Rani Chinnappa Naidu received the B.Eng. and M.Tech. degrees from VIT University, Vellore, India, and Ph.D. degree from Northumbria University, Newcastle upon Tyne, UK., all in Electrical Engineering. After that, she joined as a Postdoctoral Researcher in Northumbria Photovoltaic Applications Centre, Northumbria University, UK. She is currently an Associate Professor at VIT University. She is an Senior member in IEEE. She leads an appreciable number of research groups and projects in the areas such as solar photovoltaic, wind energy, power generation dispatch, power system optimization, and artificial intelligence techniques.