حل مسائل برنامه ریزی هوش مصنوعی با استفاده از ابزارهای وارسی مدل
  •   آرشیو : نسخه زمستان 1398
  •   نوع مقاله : پژوهشی
  •   کد پذیرش : 1349
  •   موضوع : سایر شاخه های علوم رایانه
  • نویسنده/گان : سجاد اسفندیاری، وحید رافع، محمود فرخیان
  •   کلید واژه : واژه‌های کلیدی: الگوریتم ترکیبی، بهینه‌سازی، زمانبندی‌کارها، زمان اتمام اجرای کارها.
  •   Abstract :
    A computational grid is a set of large-scale heterogeneous systems. Job scheduling is one of the most important issues in a distributed grid system. Genetic Algorithm As a basic evolutionary algorithm, it is a fair way to answer difficult problems that have failed with traditional conventional methods to achieve the desired results. This algorithm has been able to achieve acceptable results for the task scheduling problem in the grid system. In this research, the scheduling of tasks in the grid system is examined in a proposed way. Proposed method to create new evolutionary algorithms resulting from combining genetic algorithm with particle mass optimization algorithm (PSO-GA), genetic algorithm with colonial competition algorithm (GA-ICA), genetic algorithm with refrigeration simulation algorithm, SA-refrigeration algorithm (GA) Genetics deals with ant colony optimization (GA-ACO) to schedule distributed optimizations in the grid system. The main purpose of the hybrid algorithm is to improve the local search process of the genetic algorithm (GA) by combining it with the evolutionary algorithms ICA, PSO, SA and ACO, preventing premature convergence and stopping at the local minimums and ensuring global optimization. Considering the time process of workflow and completion time of works as comparison criteria, finally the proposed hybrid algorithm was able to achieve the desired results for the task scheduling problem in the distributed grid system.
  •   مراجع :
    1. Dordaie, N. and N.J. Navimipour, A hybrid particle swarm optimization and hill climbing algorithm for task scheduling in the cloud environments. ICT Express, 2018. 4(4): p. 199-202.
    2. Dai, Y., Y. Lou, and X. Lu. A Task Scheduling Algorithm Based on Genetic Algorithm and Ant Colony Optimization Algorithm with Multi-QoS Constraints in Cloud Computing. in 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics. 2015.
    3. Molaiy, S. and M. Effatparvar, Scheduling in Grid Systems using Ant Colony Algorithm. International Journal of Computer Network and Information Security, 2014. 6: p. 16-22.
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    5. Chen, T., et al., Task Scheduling in Grid Based on Particle Swarm Optimization. 2006. 238-245.
  •  صفحات : 1-10
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