Improved Genetic Algorithm Applied to Optimization of Linear Network Coding

After analyzing the intrinsic properties of the network coding, we consider the problem of minimizing the resource used for linear network coding while achieving the maximum multicast rate. Since this problem is NP-hard, we propose an improved genetic algorithm that works in an algebraic framework, combined with randomized polynomial identity testing methods, which reduces the number of nodes participating in the network coding. Some new members are added into the genetic algorithm when the new loop begins in order to avoid localized problem. Because the optimization time of the traditional genetic algorithm is too long, this paper introduces binary mutation operator to replace the traditional mutation method. We demonstrate the advantage of the proposed method over simple genetic algorithm by carrying out simulations on a number of different sets of network topologies. The experiment results show that the improved genetic algorithm has faster convergence speed and optimization speed, so it could be applied to the network coding optimization.