On the complexity of list scheduling algorithms for distributed-memory systems
ICS '99 Proceedings of the 13th international conference on Supercomputing
Scheduling Multiprocessor Tasks with Genetic Algorithms
IEEE Transactions on Parallel and Distributed Systems
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Journal of Parallel and Distributed Computing
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Triplet: A Clustering Scheduling Algorithm for Heterogeneous Systems
ICPPW '01 Proceedings of the 2001 International Conference on Parallel Processing Workshops
Improving Scheduling of Tasks in a Heterogeneous Environment
IEEE Transactions on Parallel and Distributed Systems
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Expert Systems with Applications: An International Journal
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An ant colony optimization approach, named MPAACO, for the Precedence-Constrained Heterogeneous Multiprocessor Assignment Problem (PCHMAP) is presented. The main characteristics of MPAACO are novel pheromone matrix and solution construction scheme. Separating processor selection steps from task selection steps, ant colony has full flexibility to construct new solution. Three-dimensional pheromone matrix can record each solution construction step precisely. When combined with heuristic information, they endow MPAACO the ability to find high quality schedules of PCHMAP quickly. We tested the algorithm on a set of benchmark problems from the [18]. The result shows that for 77% of all benchmark for Precedence-Constrained Homogeneous Multiprocessor Assignment Problem, a special case of PCHMAP, the algorithm can get the optimal in just one try. For PCHMAP problems, MPAACO outperforms other algorithms significantly.