Manreet Sohal et al, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.6, June- 2015, pg. 544-551 © 2015, IJCSMC All Rights Reserved 544 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 4, Issue. 6, June 2015, pg.544 – 551 RESEARCH ARTICLE A Framework for Optimizing Distributed Database Queries Based on Stochastic Fractal Search Manreet Sohal 1 , Atinderpal Singh 2 , Dr. Rajinder Singh Virk 3 1 Department of Computer Science and Technology, GNDU, Amritsar, India 2 Department of Computer Science and Technology, GNDU, Amritsar, India 3 Associate Professor, Department of Computer Science and Technology, GNDU, Amritsar, India 1 reetsohal@yahoo.com, 2 saini_amrit@live.com, 3 tovirk@yahoo.com Abstract- In this paper the problem of query optimization in distributed databases have been discussed. Query Optimization in large distributed databases is a NP-Hard natured problem and is quite difficult to solve. Communication costs for transferring data across various sited is the major cost that affects the performance of the query. Lots of research has been done on this area and number of algorithms like ant colony optimization, genetic algorithms, swarm optimization has been used to optimize the queries in distributed databases. In this paper we have provided new framework for distributed query optimization based on stochastic fractal search algorithm. In this approach two process have been used diffusion and update. In the first process new particles are created from existing particles and in the second process the position of particles are updated in order to reduce the overall communication costs of the queries. Keywords- query optimization, distributed databases, evolutionary methods, stochastic fractal search, communication costs. I. INTRODUCTION The aim of query processing in distributed database system is to translate the query from high level query (user query in non procedural languages like SQL) in distributed database to low level query (queries in procedural language like relational algebra) in which low level details are hidden from the user.[11] In a relational database, queries are solved by joining various tables(relations), but in Distributed databases, these tables are located on different dispersed sites of a computer network, therefore in order to solve these queries data has to be moved between the sites. Therefore distributed query costs consists of processing costs(i/o costs, cpu costs) and a transmission cost [3].As the size of database increases considerably the requirement for a distributed DBMS increases constantly. Communication costs required for the data transfer are the major costs of query processing in a DDBMS[2]. Query Optimization is a major aspect of query processing. Query optimization refers to process of finding the best execution plan for a given query which represents the execution strategy for the query. This QEP minimizes an objective cost function. The main objective of the query optimization is to decide the most efficient query execution plan which has minimum execution cost, among many possible plans by determining execution sequential order of relational operators. Query Optimization in distributed databases is very difficult task due to number of factors like data allocation, communication channel’s speed,