The Linear Network Coding Optimization Problems Based On Discrete Particle Swarm Optimization Algorithm

PSO(Particle Swarm Optimization, PSO)algorithm has a simple computation, fast convergence and robustness, so in the continuous problem-solving has achieved great success, in recent years on its research in the field caused by the discrete a lot of attention, according to the discrete PSO algorithm for the mapping of the different methods can be discrete, discrete particle swarm (DPSO) algorithm can divided into two categories: based on the continuous space DPSO algorithm and based on the discrete DPSO algorithm, we depth research of a discrete space-based Boolean DPSO (Boolean DPSO, BDPSO) algorithm, for its initial operation often trapped in local extreme disadvantage, so we in the velocity update formula introduces a disturbance factor in the particles out of local minimum, further more in the similarity of the particle adaptive inertia weight and learning factors effectively increase the convergence speed and accuracy, and finally by a factor of disturbance with adaptive Boolean particle swarm optimization (referred to as ABDPSO) algorithm. With the other three algorithms are functions of the six benchmarks comparing the simulation results that illustrate ABDPSO algorithm has good convergence speed and accuracy.Alshede first proposed in 2000, the concept of network coding and theory show that if the transmission of information to allow network nodes to encode, not the limited to the store and forward, to make way based on the theory of network multicast to achieve the maximum transmission capacity, we introduces the basic principles of linear network coding and common construction algorithm, including the exponential time algorithm, polynomial time algorithm and random network coding. Network coding for coding redundancy exists in the construction process, that is not all intermediate nodes in the network coding, the proposed algorithm is applied to the ABDPSO the process of network coding structure, find the edge of the network with minimal code encoding scheme and genetic algorithm by fixed topology optimization of network coding comparison test shows that the proposed algorithm can ABDPSO network coding applied to the optimization process, has some practical value.