Data allocation in distributed database systems: a novel hybrid method based on differential evolution and variable neighborhood search

Abstract

Data allocation problem (DAP) in distributed database systems is a NP-hard optimization problem with significant importance in parallel processing environments. The solution of problem aims to minimize the total cost of transactions and settlement of queries in which he main cost regards to the data transmission through the distributed system. These costs are affected by the strategy how to allocate fragments to the sites. Researchers have been solving this challenging problem by applying soft computing methods especially evolutionary algorithms. This study proposes a novel hybrid method based on differential evolution (DE) algorithm and variable neighborhood search (VNS) mechanism for solving DAP problem. The suggested hybrid method (DEVNS) aims to increase the performance of DE algorithm by applying effective selection and crossover operators. Moreover, DEVNS goals to improve the solutions found so far using VNS technique. By applying VNS, more promising parts of search space can be extracted. Eventually, the introduced DEVNS explores the search space via DE and fulfills more exploitation by neighborhood search mechanism. Performance of proposed DEVNS is experimentally evaluated against nine state-of-the-art methods using well-known benchmarks reported in literature. Obtained results exhibits that proposed DEVNS takes the first position for 13 of 20 test problems. Likewise, Friedman aligned rank test is carried out to demonstrate that there is significant statistical difference between all methods.