A Parallel Genetic Algorithm for Maximum Flow Problem

The maximum flow problem is a type of network optimization problem in the flow graph theory. Many important applications used the maximum flow problem and thus it has been studied by many researchers using different methods. Ford Fulkerson algorithm is the most popular algorithm that used to solve the maximum flow problem, but its complexity is high. In this paper, a parallel Genetic algorithm is applied to find a maximum flow in a weighted directed graph, by finding the objective function value for each augmenting path from the source to the sink simultaneously in the parallel steps in every iteration. The algorithm is implemented using Message Passing Interface (MPI) library, and results are conducted from a real distributed system IMAN1 supercomputer and were compared with a sequential version of Genetic-Maxflow. The simulation results show this parallel algorithm speedup the running time by achieving up to 50% parallel efficiency.

@article{Surakhi2017,
  title     = {A Parallel Genetic Algorithm for Maximum Flow Problem},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {6},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Ola M. Surakhi and Mohammad Qatawneh and Hussein A. al Ofeishat},
  doi       = {10.14569/IJACSA.2017.080620},
  url       = {https://doi.org/10.14569/IJACSA.2017.080620}
}