A Heterogeneous Multiprocessor Independent Task Scheduling Algorithm Based on Improved PSO

  • Xiaohui Cheng
  • Fei DaiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)


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.


Task 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.


  1. 1.
    Iturriaga, S., et al.: A parallel local search in CPU/GPU for scheduling independent tasks on large heterogeneous computing systems. J. Supercomput. 71(2), 648–672 (2014)CrossRefGoogle Scholar
  2. 2.
    Sahni, S.K.: Algorithms for scheduling independent tasks. J. ACM 23(1), 116–127 (1976)MathSciNetCrossRefGoogle Scholar
  3. 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
  4. 4.
    Yi, J., et al.: Reliability-guaranteed task assignment and scheduling for heterogeneous multiprocessors considering timing constraint. J. Signal Process. Syst. 81(3), 359–375 (2014)CrossRefGoogle Scholar
  5. 5.
    Kumar, N., Vidyarthi, D.P.: A novel hybrid PSO–GA meta-heuristic for scheduling of DAG with communication on multiprocessor systems. Eng. Comput. 32(1), 35–47 (2015)CrossRefGoogle Scholar
  6. 6.
    Xu, Y., Li, K., Hu, J., et al.: A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf. Sci. 270(6), 255–287 (2014)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Ayari, R., et al.: ImGA: an improved genetic algorithm for partitioned scheduling on heterogeneous multi-core systems. Des. Autom. Embed. Syst. 22(1–2), 183–197 (2018)CrossRefGoogle Scholar
  8. 8.
    Jiang, Y., et al.: DRSCRO: a metaheuristic algorithm for task scheduling on heterogeneous systems. Math. Probl. Eng. 2015, 1–20 (2015)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Prescilla, K., Immanuel Selvakumar, A.: Modified Binary Particle Swarm optimization algorithm application to real-time task assignment in heterogeneous multiprocessor. Microprocess. Microsyst. 37(6–7), 583–589 (2013)CrossRefGoogle Scholar
  10. 10.
    Xie, G., et al.: Mixed real-time scheduling of multiple DAGs-based applications on heterogeneous multi-core processors. Microprocess. Microsyst. 47, 93–103 (2016)CrossRefGoogle Scholar
  11. 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
  12. 12.
    Xu, Y., et al.: A DAG scheduling scheme on heterogeneous computing systems using double molecular structure-based chemical reaction optimization. J. Parallel Distrib. Comput. 73(9), 1306–1322 (2013)CrossRefGoogle Scholar
  13. 13.
    Rzadca, K., Seredynski, F.: Heterogeneous multiprocessor scheduling with differential evolution. In: IEEE Congress on Evolutionary Computation (2005)Google Scholar
  14. 14.
    Gogos, C., et al.: Scheduling independent tasks on heterogeneous processors using heuristics and Column Pricing. Future Gener. Comput. Syst. 60, 48–66 (2016)CrossRefGoogle Scholar
  15. 15.
    Braun, T.D., et al.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61(6), 810–837 (2001)CrossRefGoogle Scholar
  16. 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. 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. 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. 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
  20. 20.
    Sarathambekai, S., Umamaheswari, K.: Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem. J. Algorithms Comput. Technol. 11(1), 58–67 (2016)MathSciNetCrossRefGoogle Scholar
  21. 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. 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

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Guangxi Key Laboratory of Embedded Technology and Intelligent System, College of Information Science and EngineeringGuilin University of TechnologyGuilinChina

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