A Heterogeneous Multiprocessor Independent Task Scheduling Algorithm Based on Improved PSO
The independent task scheduling problem of heterogeneous multi-processors belongs to the NP-hard problem. The emergence of evolutionary algorithms provides a new idea for solving this problem. Particle swarm optimization (PSO) is a kind of intelligent evolutionary algorithm and it could be used to solve scheduling problem. We firstly discretized the representation of particle swarm optimization algorithm and made it suitable for the scheduling problem of heterogeneous multiprocessors. Then, the PSO algorithm was introduced into heterogeneous multiprocessors independent task scheduling problem by modeling method. In order to overcome particle swarm optimization algorithm’s problem that is easy to fall into local optimum and premature convergence. We proposed a heterogeneous multiprocessor independent task scheduling algorithm based on improved PSO by improving the update operation of particle swarm optimization algorithm and transformed it into crossover and mutation operation of genetic algorithm. The experimental results show that the improved PSO scheduling algorithm can overcome the premature defects of PSO algorithm and the makespan of proposed IPSO is smaller than PSO.
KeywordsTask scheduling Independent tasks Particle swarm optimization Heterogeneous multiprocessors
As the research of the thesis is sponsored by National Natural Science Foundation of China (No: 61662017, No: 61262075), Key R & D projects of Guangxi Science and Technology Program (AB17195042), Guangxi Science and Technology Development Special Science and Technology Major Project (No: AA18118009), Guangxi Key Laboratory Fund of Embedded Technology and Intelligent System, we would like to extend our sincere gratitude to them.
- 3.Shriya, S., et al.: Directed search-based PSO algorithm and its application to scheduling independent task in multiprocessor environment 404, 23–31 (2016)Google Scholar
- 11.Xu, C., Li, T.: Chemical reaction optimization for task mapping in heterogeneous embedded multiprocessor systems. Adv. Mater. Res. 712–715, 2604–2610 (2013)Google Scholar
- 13.Rzadca, K., Seredynski, F.: Heterogeneous multiprocessor scheduling with differential evolution. In: IEEE Congress on Evolutionary Computation (2005)Google Scholar
- 16.Dorronsoro, B., Pinel, F.: Combining machine learning and genetic algorithms to solve the independent tasks scheduling problem. In: IEEE International Conference on Cybernetics (2017)Google Scholar
- 17.Zhou, Y., Jiang, C., Fang, Y.: Research on independent task scheduling algorithm in heterogeneous environment. Comput. Sci. 35(8), 90–92+97 (2008)Google Scholar
- 18.Omidi, A., Rahmani, A.M.: Multiprocessor independent tasks scheduling using a novel heuristic PSO algorithm. In: IEEE International Conference on Computer Science and Information Technology, pp. 369–373. IEEE (2009)Google Scholar
- 19.Zhang, W., et al.: Energy-aware real-time task scheduling for heterogeneous multiprocessors with particle swarm optimization algorithm. In: Mathematical Problems in Engineering, pp. 1–9 (2014)Google Scholar
- 21.Chen, J., Pan, Q.: Improved particle swarm optimization algorithm for solving independent task scheduling problem. Microelectron. Comput. 34(6), 214–215 (2008)Google Scholar
- 22.Wang, Y., Wang, N., Yang, C., et al.: A discrete particle swarm optimization algorithm for task assignment problem. J. Cent. South Univ. (Sci. Technol.) 39(3), 571–576 (2008)Google Scholar