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The Scientific World Journal
Volume 2014, Article ID 628471, 17 pages
Research Article

Distributed Query Plan Generation Using Multiobjective Genetic Algorithm

1SFIO-NIC Division, National Informatics Center, Ministry of Information Technology, New Delhi 110003, India
2School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India

Received 23 August 2013; Accepted 23 January 2014; Published 14 May 2014

Academic Editors: L. D. S. Coelho, Y. Jiang, W. Su, and G. A. Trunfio

Copyright © 2014 Shina Panicker and T. V. Vijay Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.