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The shortest path problem in the stochastic networks with unstable topology.

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A genetic algorithm for the arrival probability in the stochastic networks.

Gholam H Shirdel1, Mohsen Abdolhosseinzadeh1

  • 1Department of Mathematics, Faculty of Basic Science, University of Qom, Qom, Iran.

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|June 29, 2016
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Summary
This summary is machine-generated.

This study introduces a genetic algorithm to enhance transmission reliability in delay-sensitive networks by accurately calculating arrival probabilities in stochastic networks. The method improves flow reliability for large-scale systems.

Keywords:
Arrival probabilityDiscrete time Markov chainGenetic algorithmStochastic network

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Area of Science:

  • Computer Science
  • Network Engineering
  • Operations Research

Background:

  • Delay-sensitive networks require reliable transmission flow.
  • Stochastic network parameters complicate arrival probability calculations.
  • Existing methods are inadequate for large-scale stochastic networks.

Purpose of the Study:

  • To develop a genetic algorithm for calculating arrival probability in directed acyclic networks with stochastic parameters.
  • To improve the reliability of transmission flow in delay-sensitive networks.
  • To provide a scalable solution for analyzing large stochastic networks.

Main Methods:

  • A genetic algorithm is employed to identify optimal network connections.
  • Sub-networks are extracted, and connections are established between source and destination nodes.
  • Discrete-time Markov chains model network transitions, defining arrival probability as multi-step transition probability.

Main Results:

  • The genetic algorithm successfully determines connections with maximum arrival probability.
  • The proposed method achieves perfect fitness values, demonstrating effectiveness.
  • Numerical results validate the algorithm's applicability to large stochastic networks.

Conclusions:

  • The genetic algorithm offers a reliable method for calculating arrival probability in complex stochastic networks.
  • This approach enhances transmission flow reliability, particularly for delay-sensitive applications.
  • The method overcomes limitations of previous techniques for large-scale network analysis.