SWARM INTELLIGENCE SCHEDULING OF SOFT REAL-TIME TASKS IN HETEROGENEOUS MULTIPROCESSOR SYSTEMS
Authors/Creators
- 1. Department of Computer Engineering, Nobonyad High Education Institute, Sirjan, Iran
- 2. Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Shiraz, Iran
- 3. Department of Electrical Engineering, Shahid Beheshti University G.C., Tehran, Iran
Description
In this paper, a hybrid swarm intelligence algorithm (named VNABCSA) is presented for the scheduling of non-preemptive soft real-time tasks in heterogeneous multiprocessor platforms. The method is based on a combination of artificial bee colony and simulated annealing algorithms. The multi-objective function of the VNABCSA algorithm is defined to minimize the total tardiness of all tasks, total number of utilized processors, total completion time, total waiting time for all tasks, and total waiting time for all processors. We introduce a hybrid variable neighborhood search strategy to improve the convergence speed of the algorithm. Simulation results demonstrate the efficiency of the proposed methodology as compared with the existing scheduling algorithms.