Kusum Deep

Work place: Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee – 247667, India

E-mail: kusumdeep@gmail.com

Website: https://www.researchgate.net/profile/Kusum-Deep-2

Research Interests: Combinatorial Optimization, Analysis of Algorithms, Data Structures and Algorithms, Artificial Intelligence, Computational Science and Engineering


Dr. Kusum Deep, is a full Professor, with the Department of Mathematics, Indian Institute of Technology Roorkee, India. Born on August 15, 1958, she pursued B.Sc Hons and M.Sc Hons. School from Centre for Advanced Studies, Panjab University, Chandigarh. An M.Phil Gold Medalist, she earned her PhD from IIT Roorkee in 1988, assisted by UGC Scholarship throughout. She carried out Post-Doctoral Research at Loughborough University, UK during 1993-94, under an International Post Doctorate Bursary funded by Commission of European Communities, Brussels. She was awarded the Khosla Research Award in 1991; UGC Career Award in 2002; Starred Performer of IIT – Roorkee Faculty continuously from 2001 to 2005; best paper, Railway Bulletin of Indian Railways, 2005; special facilitation in memory of late Prof. M. C. Puri, 2007. She has co-authored a book entitled "Optimization Techniques" by New Age Publishers New Delhi in 2009 with an International edition by New Age Science, UK. Eleven students have been awarded PhD under her supervision and six are in progress. She has 80 research publications in refereed International Journals and 60 research papers in International / National Conferences. She is on the editorial board of a number of International and National Journals. She is a Senior Member of Operations Research Society of India, IEEE, Computer Society of India, Indian Mathematical Society and Indian Society of Industrial Mathematics. She is on the Expert Panel of the Department of Science and Technology, Govt. of India. She is the Executive Editor of International Journal of Swarm Intelligence, Inderscience. She is the Founder President of Soft Computing Research Society, India and the secretary of Forum of Interdisciplinary Mathematics. Her areas of specialization are numerical optimization and their applications to engineering, science and industry. Currently her research interests are Nature Inspired Optimization Techniques, particularly, Genetic Algorithms, Memetic Algorithms, Particle Swarm Optimization, Artificial Bee Colony, Biogeographical Based Optimization, Glowworm Optimization, and their applications to solve real life problems.

Author Articles
Real Coded Genetic Algorithm Operators Embedded in Gravitational Search Algorithm for Continuous Optimization

By Amarjeet Singh Kusum Deep

DOI: https://doi.org/10.5815/ijisa.2015.12.01, Pub. Date: 8 Nov. 2015

The objective of this paper is to propose three modified versions of the Gravitational Search Algorithm for continuous optimization problems. Although the Gravitational Search Algorithm is a recently introduced promising memory-less heuristic but its performance is not so satisfactory in multimodal problems particularly during the later iterations. With a view to improve the exploration and exploitation capabilities of GSA, it is hybridized with well-known real coded genetic algorithm operators. The first version is the hybridization of GSA with Laplace Crossover which was initially designed for real coded genetic algorithms. The second version is the hybridization of GSA with Power Mutation which also was initially designed for real coded genetic algorithms. The third version hybridizes the GSA with both the Laplace Crossover and the Power mutation. The performance of the original GSA and the three proposed variants is investigated over a set of 23 benchmark problems considered in the original paper of GSA. Next, all the four variants are implemented on 30 rotated and shifted benchmark problems of CEC 2014. The extensive numerical, graphical and statistical analysis of the results show that the third version incorporating the Laplace Crossover and Power mutation is a definite improvement over the other variants.

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