Hierarchic vs. Single–Population and Hybrid Metaheuristic Grid Schedulers: A Comparative Empirical Study

Part of the Studies in Computational Intelligence book series (SCI, volume 419)

Abstract

This chapter presents the results of comprehensive empirical evaluation of hierarchical, hybrid, single- and multi-population genetic metaheuristics in static and dynamic versions of the scheduling problem in grid. All metaheuristics have been integrated with the Sim-G-Batch grid simulator. The results of the analysis show the high effectiveness of HGS-Sched in exploration of the bi-objective dynamic optimization landscapes in highly-parametrized grids.

Keywords

Genetic Algorithm Tabu Search Tabu Search Algorithm Grid Simulator Tuning Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of Computer Science Cracow University of TechnologyCracowPoland

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