(untitled) (مجلة جامعة حماة, 2024)
Distributed query processing entails accessing data from multiple sites. keeping in mind that the distribution of the database should be transparent to user .In addition to the usual disk IO and CPU costs, the cost due to transmission of data between different sites, referred to as the site-to-site communication cost, also exists. This cost, being the major cost, needs to be reduced in order to improve the response time for distributed queries. One way to reduce this communication cost is by devising a distributed query processing strategy that involves fewer number of sites for answering the distributed queries. In this paper, a distributed query plan generation (DQPG) algorithm based FAGA algorithm, which is a combination between the luminous Firefly Algorithm( FA) and the Genetic Algorithm (GA), which generates distributed query plans that involves less number of sites and have higher relation concentration in the participating sites, is presented. Additionally, the experimental comparison of the FAGA algorithm with the GA,FA and artificial bee colony (ABC) algorithms in terms of cost rate algorithm exhibits that the former is able to generate comparatively better quality top-K query plans for a given distributed query.