C. Patvardhan

Work place: Faculty of Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra. 282005

E-mail: cpatvardhan@gmail.com

Website: https://scholar.google.com/citations?user=HNNXOyYAAAAJ&hl=en

Research Interests: Image Compression, Image Manipulation, Image Processing, Quantum Computing Theory


C. Patvardhan is working in the Dayalbagh Educational Institute, Agra as Professor in Electrical Engineering and Coordinator of the PG program in Faculty of Engineering, DEI. He obtained his BSc (Engg.) degree from Dayalbagh in 1987, MTech from IISc, Bangalore in Computer Science in 1989 and his PhD from DEI in 1994. He has published more than 250 papers in journals and proceedings of conferences and has won 20 Best Paper Awards. He has also published one book and has been an Editor of three Conference proceedings. He has been the Investigator/Co-Investigator of several funded R&D projects. He has been a consultant on Advanced Algorithms to various EDA industries. His current research interests are quantum and soft computing and image processing.

Author Articles
Balanced Quantum-Inspired Evolutionary Algorithm for Multiple Knapsack Problem

By C. Patvardhan Sulabh Bansal Anand Srivastav

DOI: https://doi.org/10.5815/ijisa.2014.11.01, Pub. Date: 8 Oct. 2014

0/1 Multiple Knapsack Problem, a generalization of more popular 0/1 Knapsack Problem, is NP-hard and considered harder than simple Knapsack Problem. 0/1 Multiple Knapsack Problem has many applications in disciplines related to computer science and operations research. Quantum Inspired Evolutionary Algorithms (QIEAs), a subclass of Evolutionary algorithms, are considered effective to solve difficult problems particularly NP-hard combinatorial optimization problems. A hybrid QIEA is presented for multiple knapsack problem which incorporates several features for better balance between exploration and exploitation. The proposed QIEA, dubbed QIEA-MKP, provides significantly improved performance over simple QIEA from both the perspectives viz., the quality of solutions and computational effort required to reach the best solution. QIEA-MKP is also able to provide the solutions that are better than those obtained using a well known heuristic alone.

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