Design and Applications of Nature Inspired Optimization (eBook)

Contribution of Women Leaders in the Field

Dipti Singh, Vanita Garg, Kusum Deep (Herausgeber)

eBook Download: PDF

2023 | 1st ed. 2022
X, 204 Seiten
Springer International Publishing (Verlag)
978-3-031-17929-7 (ISBN)

Infos

This book gives a detailed information of various real-life applications from various fields using nature inspired optimization techniques. These techniques are proven to be efficient and robust in many difficult problems in literature. The authors provide detailed information about real-life problems and how various nature inspired optimizations are applied to solve these problems. The authors discuss techniques such as Biogeography Based Optimization, Glow Swarm Optimization, Elephant herd Optimization Algorithm, Cuckoo Search Algorithm, Ant Colony Optimization, and Grey Wolf Optimization etc. These algorithms are applied to a wide range of problems from the field of engineering, finance, medicinal etc. As an important part of the Women in Science and Engineering book series, the work highlights the contribution of women leaders in nature inspired optimization, inspiring women and men, girls and boys to enter and apply themselves to the field.

Dr. Dipti Singh, received her PhD from the Indian Institute of Technology, Roorkee studying Optimization Techniques. She currently works at the Department of Applied Mathematics, Gautam Buddha University. She does research in Applied Mathematics. Her area of interest is applications of soft computing techniques in various disciplines of engineering. Her current project is 'forecasting and Analysis of Solid Waste and its Management in India.

Dr. Vanita Garg, received her PhD from the Indian Institute of Technology, Roorkee, studying Applied Mathematics. She teaches at Galgotia University, specializing in Nature Inspired Optimization Techniques. Her research areas include Operational Research, Unconstrained Optimization Problems, Constrained Optimization Problems, and Biogeography Based Optimization Algorithm.

Dr. Kusum Deep, is a full Professor, with the Department of Mathematics, Indian Institute of Technology Roorkee, India and Visiting Professor, Liverpool Hope University, UK and University of Technology Sydney, Australia. With B.Sc Hons & M.Sc Hons. School from Centre for Advanced Studies, Panjab University, Chandigarh, she is an M.Phil Gold Medalist. She earned her PhD from UOR (now IIT Roorkee) in 1988. She has been a national scholarship holder and a Post Doctoral from Loughborough University, UK assisted by International Bursary funded by Commission of European Communities, Brussels. She has won numerous awards like Khosla Research Award, UGC Career Award, Starred Performer of IITR Faculty, best paper awards by Railway Bulletin of Indian Railways, special facilitation in memory of late Prof. M. C. Puri, AIAP Excellence Award. She has authored two books, supervised 20 PhDs, and published 125 research papers. She is a Senior Member of ORSI, CSI, IMS and ISIM. She is the Executive Editor of International Journal of Swarm Intelligence, Inderscience. She is Associate Editor of Swarm and Evolutionary Algorithms, Elsevier and is on the editorial board of many journals. She is the Founder President of Soft Computing Research Society, India. She is the General Chair of series of International Conference on Soft Computing for Problems Solving (SocProS). Her research interests are Evolutionary Algorithms, Swarm Intelligence and nature inspired optimization techniques and their applications.

Produkt-Details

• Erscheint lt. Verlag: 2.1.2023
• Reihe/Serie: Women in Engineering and Science
• Reihe/Serie: Women in Engineering and Science
• Zusatzinfo: X, 204 p. 65 illus., 58 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• Themenwelt: Mathematik / Informatik ► Mathematik
• Themenwelt: Technik
• Schlagworte: Nature inspired Optimization • Optimization Problems • SDG5 • Women in science and engineering • Women in STEM • Women in Technology
• ISBN-10: 3-031-17929-3 / 3031179293
• ISBN-13: 978-3-031-17929-7 / 9783031179297