Multiprocessor task scheduling in multistage hybrid flow-shops: A parallel greedy algorithm approach. Applied Soft Computing, 10(4):1293-1300, 2010.Serifoglu, F. Sivrikaya,Ulusoy, G.Multiprocessor task schedulin
We evaluate the achieved makespan reduction of different parallel applications, relatively to the results obtained by the best greedy algorithm in the literature, as a function of parameters such as problem size, system heterogeneity, and number of processors. Our results show that the parallel tabu...
For addressing the MTP problem, we introduce a novel solving algorithm that decomposes the issue into two components: optimal collision-free path planning and task scheduling optimization. In terms of collision-free path planning, the task sequence optimization resembles the Traveling Salesman Problem21...
The problem is solved using two new implementations of Tabu Search and genetic algorithm presented in the paper. A new approach to solution coding is also introduced and implemented in both metaheuristics algorithms. Results given by the algorithms are compared to those generated by greedy LPT and ...
A. (2023). Resource management in fog computing using greedy and semi-greedy spider monkey optimization. Soft Computing. https://doi.org/10.1007/s00500-023-09123-7. Article Google Scholar Khiat, A., Haddadi, M., & Bahnes, N. (2024). Genetic-based algorithm for task scheduling in ...
Utilizing the trained vector\(\overline{\theta }\), we can conduct H-ADP algorithm for task scheduling. Meanwhile, the C-H algorithm can be used standalone in scheduling tasks as a kind of myopic or greedy algorithm. There are also two commonly used algorithms, load-balancing and randomized...
The above recursive greedy algorithm can be expressed in an iterative manner. In task scheduling, we repeat the same process again and again, so iteration of the same process is computationally efficient. In this case, we have two tasks, s is set of all the tasks, and f is the set of...
[10], with a vehicle selection mechanism and a task offloading decision algorithm. The optimization model is established to increase the task execution efficiency. It is solved by the greedy algorithm and the discrete bat algorithm, respectively, under the scenarios set in this article. The random...
Pham et al.[114]presented a taskscheduling algorithmin the Fog-Cloud environment. The proposed algorithm performs the scheduling by specifying the priorities of tasks, and determining which node to execute tasks. The obtained results show that the proposed algorithm provides an efficient balance betwee...
have proposed a novel Multi-objective algorithm to schedule tasks on a cloud environment named MGGS (modified genetic algorithm (GA) combined with greedy strategy). The MGGS algorithm was evaluated based on total completion time, average response time, and QoS parameters. The proposed genetic ...