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Heuristic Hybrid Genetic and Simulated Annealing Algorithms with Neural Networks for Task Assignment in Heterogeneous Computing Systems

Mahdavi-Amiri, Ali | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 39357 (02)
  4. University: Sharif University of Technology
  5. Department: Mathematical Sciences
  6. Advisor(s): Mahdavi Amiri, Nezamoddin
  7. Abstract:
  8. In this thesis, we want to present methods that are able to solve the assignment tasks problem in a heterogeneous computing system. These methods are two hybrid methods that are constructed by composing Hopefield Neural Networks with Genetic Algorithms and the Simulated Annealing. First, we solve the relaxed problem by applying Genetic Algorithms and the Simulated Annealing and we compare the results of these ways with other traditional methods. Then, we solve the constrained problem with mentioned hybrid methods. The definition of the problem is as following: Consider a distributed computing system which is comprised of set of processors with different speeds but the same structure. We want to assign tasks to processors in an efficient way that the problem’s cost function will be minimized. We also want that the sum of resources needed by tasks that run on a processor is less that the processor’s constraint.In the first three chapters of this thesis, we present information about Genetic Algorithms, the Simulated Annealing and Neural Networks respectively. In the final chapter, we present the problem completely. We present solutions and their definitions and present the results of each of them
  9. Keywords:
  10. Genetic Algorithm ; Hopfield Nearal Network ; Simulated Annealing Method ; Task Assignment

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