A Nested Genetic Algorithm for Mobile Ad-Hoc Network Optimization with Fuzzy Fitness

One of the major culprits that faces Mobile Ad-hoc networks (MANET) is broadcasting, which constitutes a very important part of the infrastructure of such networks. This paper presents a nested genetic algorithm (GA) technique with fuzzy logic-based fitness that optimizes the broadcasting capability of such networks. While normally the optimization of broadcasting is considered as a multi-objective problem with various output parameters that require tuning, the proposed system taps another approach that focuses on a single output parameter, which is the network reachability time. This is the time required for the data to reach a certain percentage of connected clients in the network. The time is optimized by tuning different decision parameters of the Delayed Flooding with Cumulative Neighborhood (DFCN) broadcasting protocol. The proposed system is developed and simulated with the help of the Madhoc network simulator and is applied on different realistic real-life scenarios. The results reveal that the reachability time responds well to the suggested system and shows that each scenario responds differently to the tuning of decision parameters.

@article{Eissa2019,
  title     = {A Nested Genetic Algorithm for Mobile Ad-Hoc Network Optimization with Fuzzy Fitness},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {9},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {NourElDin S Eissa and Ahmed Zakaria Talha and Ahmed F. Amin and Amr Badr},
  doi       = {10.14569/IJACSA.2019.0100928},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100928}
}