Genetic Algorithm (GA) for Multiprocessors Scheduling

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    In a multiprocessors environment, an algorithm is used to effectively delegate tasks to the processors. The algorithm act as the resource distributor and controller to ensure that the processors are equally utilized thus optimizing the usage of the processors. Thus, the objectives of the study are to replicate the Genetic Algorithm (GA) and use the algorithm to shorten the timespan for the parallel task to be completed. The algorithm has the ability to improve and find the optimum solution based on the population needs. Hence, C++ is used as the platform for the simulation. Experimental results validate that the designed program has the ability to replicate the GA in achieving the objectives of the study.


  • Keywords

    Genetic Algorithm; Multiprocessors; Parallel Processors

  • References

      [1] Caswell, D. J., & Lamont, G. B. (2003, December). Distributed processor allocation for discrete event simulation and digital signal processing using a multiobjective evolutionary algorithm. In Evolutionary Computation, 2003. CEC'03. The 2003 Congress on (Vol. 3, pp. 1803-1810). IEEE.

      [2] Bazoobandi, H. A., Khorashadizadeh, M., & Eftekhari, M. (2014, February). Solving task scheduling problem in multi-processors with genetic algorithm and task duplication. In Intelligent Systems (ICIS), 2014 Iranian Conference on (pp. 1-4). IEEE.

      [3] McCreary, C. L., Khan, A. A., Thompson, J. J., & McArdle, M. E. (1994, April). A comparison of heuristics for scheduling DAGs on multiprocessors. In Parallel Processing Symposium, 1994. Proceedings., Eighth International (pp. 446-451). IEEE.

      [4] Hoseinpour, A., Lahijani, M. J., Hoseinpour, M., & Kazemitabar, J. (2018). Fitness function improvement of evolutionary algorithms used in sensor network optimisations. IET Networks, 7(3), 91-94.

      [5] Khan, Z. A., Siddiqui, J., & Samad, A. (2016, March). A novel task scheduling algorithm for parallel system. In Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on (pp. 3983-3986). IEEE.

      [6] Albacea, E. A. (1997, April). An optimal parallel algorithm for two-processor scheduling. In High Performance Computing on the Information Superhighway, 1997. HPC Asia'97 (pp. 220-223). IEEE.

      [7] Ahmad, I., Kwok, Y. K., & Wu, M. Y. (1996, June). Analysis, evaluation, and comparison of algorithms for scheduling task graphs on parallel processors. In Parallel Architectures, Algorithms, and Networks, 1996. Proceedings., Second International Symposium on (pp. 207-213). IEEE.

      [8] Kwok, Y. K., Ahmad, I., & Gu, J. (1996, August). FAST: A low-complexity algorithm for efficient scheduling of DAGs on parallel processors. In Parallel Processing, 1996. Vol. 3. Software., Proceedings of the 1996 International Conference on (Vol. 2, pp. 150-157). IEEE.

      [9] Kaur, J., Singh, S., & Singh, S. (2016, February). Parallel Implementation of PSO Algorithm Using GPGPU. In Computational Intelligence & Communication Technology (CICT), 2016 Second International Conference on (pp. 155-159). IEEE.

      [10] Faber, Ł., & Boryczko, K. (2016, September). Efficient parallel execution of genetic algorithms on Epiphany manycore processor. In Computer Science and Information Systems (FedCSIS), 2016 Federated Conference on (pp. 865-872). IEEE.

      [11] Lassabe, N. (2013, December). Optimized SCC Processor by Using Parallel Genetic Algorithms. In Technologies and Applications of Artificial Intelligence (TAAI), 2013 Conference on (pp. 403-407). IEEE.




Article ID: 17525
DOI: 10.14419/ijet.v7i3.15.17525

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.