A GA(TS) Hybrid Algorithm for Scheduling in Computational Grids

Fatos Xhafa, Juan A. Gonzalez, Keshav P. Dahal, Ajith Abraham

Research output: Chapter in Book/Report/Conference proceedingChapter

30 Citations (Scopus)


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 the GA and TS for the makespan value but cannot outperform them for the flowtime of the scheduling.
Original languageEnglish
Publication statusPublished - 2009

Publication series

NameLecture Notes in Artificial Intelligence

Cite this

Xhafa, F., Gonzalez, J. A., Dahal, K. P., & Abraham, A. (2009). A GA(TS) Hybrid Algorithm for Scheduling in Computational Grids. In HYBRID ARTIFICIAL INTELLIGENCE SYSTEMS (Vol. 5572, pp. 285-292). (Lecture Notes in Artificial Intelligence). http://link.springer.com/chapter/10.1007%2F978-3-642-02319-4_34