A Novel Evolutionary Algorithm for Solving Static Data Allocation Problem in Distributed Database Systems

Given a distributed database system and a set of queries from each site, the objective of a data allocation algorithm is to locate the data fragments at different sites so as to minimize the total data transfer cost incurred in executing the queries. The data allocation problem, however, is NP-complete, and thus requires fast heuristics and random approaches to generate efficient solutions. In this paper an approximate algorithm has been proposed. This algorithm is a hybrid evolutionary algorithm obtained from combining object migration learning automata and genetic algorithm. Experimental results show that proposed algorithm has significant superiority over the several well-known methods.