### Abstract

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.

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 |

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### Keywords

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

### Cite this

*Proceedings of the 23rd Workshop of the UK Planning and Scheduling Special Interest Group*

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*Proceedings of the 23rd Workshop of the UK Planning and Scheduling Special Interest Group.*

**A hybrid ant algorithm for scheduling independent jobs in heterogeneous computing environments.** / Ritchie, G.; Levine, J.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - A hybrid ant algorithm for scheduling independent jobs in heterogeneous computing environments

AU - Ritchie, G.

AU - Levine, J.

PY - 2004/12/1

Y1 - 2004/12/1

N2 - 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) anNP-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.

AB - 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) anNP-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.

KW - hybrid ant algorithm

KW - heterogeneous computing environments

KW - ant colony optimization

M3 - Conference contribution book

BT - Proceedings of the 23rd Workshop of the UK Planning and Scheduling Special Interest Group

ER -