Anand Srivastav

Work place: Christian-Albrechts-Universität zu Kiel, Institut für Informatik, Christian-Albrechts-Platz 4, 24118 Kiel, Germany



Research Interests: Computer Science & Information Technology, Applied computer science, Computer systems and computational processes, Computer Architecture and Organization, Theoretical Computer Science


Anand Srivastav is a Professor in the Department of Computer Science, Christian-Albrechts-Universitat zu Kiel, Germany. He obtained his doctorate from the University of Munster in 1988. Thereafter, he worked as an Assistant Professor in the University of Bonn, the New York University, Yale University, the Free University, Berlin and Humboldt University of Berlin. He has held senior positions at several institutions, and has been director of the Computational Science Center at Christian-Albrechts-Universitat zu Kiel since 2007. He was a speaker of the DFG Research Training Group, “Efficient Algorithms and Multiscale Methods” from 2000–2005, and has been the PI of the German Research Foundation’s Future Ocean Cluster at Christian-Albrechts-Universitat since 2006. He is also the PI for the “Materials for Life” Cluster of Excellence proposed in 2010 at Christian-Albrechts-Universitat, Kiel.

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.

[...] Read more.
Other Articles