A Novel Genetic Algorithm Using Simulated Annealing for Static Task Scheduling in Distributed Systems
The static task scheduling problem in distributed systems is very important because of optimal usage of available machines and accepted computation time for scheduling algorithm. Solving this problem using the dynamic programming and the back tracking needs much more time. Therefore, there are more attempts to solve it using the heuristic methods. In this paper, a new genetic algorithm, named TDGASA, is presented which its running time depends on the number of tasks in the scheduling problem. Then, the computation time of TDGASA to find a sub-optimal schedule is improved by Simulated Annealing (SA).