Genetic algorithm based heuristics for the mapping problem
Computers and Operations Research - Special issue on genetic algorithms
Optimal task allocation in distributed systems by graph matching and state space search
Journal of Systems and Software
Efficient Local Search for DAG Scheduling
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Branch-and-bound task allocation with task clustering-based pruning
Journal of Parallel and Distributed Computing
Task allocation for maximizing reliability of distributed systems: a simulated annealing approach
Journal of Parallel and Distributed Computing
IEEE Transactions on Computers
Theoretical advances in artificial immune systems
Theoretical Computer Science
Computers and Industrial Engineering
A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems
Computer Standards & Interfaces
Journal of Parallel and Distributed Computing
Honey bees mating optimization algorithm for the Euclidean traveling salesman problem
Information Sciences: an International Journal
A hybrid heuristic-genetic algorithm for task scheduling in heterogeneous processor networks
Journal of Parallel and Distributed Computing
Characterizing Task-Machine Affinity in Heterogeneous Computing Environments
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
An application of swarm intelligence to distributed image retrieval
Information Sciences: an International Journal
IEEE Computational Intelligence Magazine
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
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Effective task assignment is essential for achieving high performance in heterogeneous distributed computing systems. This paper proposes a new technique for minimizing the parallel application time cost of task assignment based on the honeybee mating optimization (HBMO) algorithm. The HBMO approach combines the power of simulated annealing, genetic algorithms, and an effective local search heuristic to find the best possible solution to the problem within an acceptable amount of computation time. The performance of the proposed HBMO algorithm is shown by comparing it with three existing task assignment techniques on a large number of randomly generated problem instances. Experimental results indicate that the proposed HBMO algorithm outperforms the competing algorithms.