## Abstract

The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. Finding optimal schedules for such an environment is (in general) an

NP-hard problem, and so heuristic approaches must be used. In this paper we describe an ant colony optimisation (ACO) algorithm that, when combined with local and tabu search, can find shorter schedules on benchmark problems than other techniques found in the literature.

NP-hard problem, and so heuristic approaches must be used. In this paper we describe an ant colony optimisation (ACO) algorithm that, when combined with local and tabu search, can find shorter schedules on benchmark problems than other techniques found in the literature.

Original language | English |
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Title of host publication | Proceedings of the 23rd Workshop of the UK Planning and Scheduling Special Interest Group |

Number of pages | 7 |

Publication status | Published - 1 Dec 2004 |

## Keywords

- hybrid ant algorithm
- heterogeneous computing environments
- ant colony optimization