2026-04-18T00:30:49Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1198992026-01-23T04:57:29Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Xhafa Xhafa, Fatos author González, Juan A. author Dahal, Keshav P. author Abraham, Ajith author 2009 The hybridization of heuristics methods aims at exploring the synergies among stand alone heuristics in order to achieve better results for the optimization problem under study. In this paper we present a hybridization of Genetic Algorithms (GAs) and Tabu Search (TS) for scheduling in computational grids. The purpose in this hybridization is to benefit the exploration of the solution space by a population of individuals with the exploitation of solutions through a smart search of the TS. Our GA(TS) hybrid algorithm runs the GA as the main algorithm and calls TS procedure to improve individuals of the population. We evaluated the proposed hybrid algorithm using different Grid scenarios generated by a Grid simulator. The computational results showed that the hybrid algorithm outperforms both the GA and TS for the makespan value but cannot outperform them for the flowtime of the scheduling. Peer Reviewed Postprint (author's final draft) Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat Computational grids (Computer systems) Genetic algorithms Tabu search Computational grid Hybrid algorithm Grid resource Tabu search algorithm Computació distribuïda Algorismes genètics A GA(TS) hybrid algorithm for scheduling in computational grids