Sulabh Bansal

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



Research Interests: Computer systems and computational processes, Parallel Computing, Data Structures and Algorithms, Combinatorial Optimization


Sulabh Bansal is a Research Associate and Ph.D. scholar with the Department of Electrical Engineering, Dayalbagh Educational Institute, Agra. He obtained BE (Hons.)(Computer Science and Engineering) in 2000 and MTech in 2011. He has published several research papers in various national and international conferences and journals. He has rich 12 years experience diversified in software industry as developer and team leader and in academics as an educator and researcher. He has successfully managed several software development projects in the industry and guided several B.Tech. and M.Tech. level projects. His current research interests are evolutionary algorithms, optimization problems and parallel programming.

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

By C. Patvardhan Sulabh Bansal Anand Srivastav

DOI:, 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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