TT&C communication network survivability research methods based on simulated annealing particle swarm optimization algorithm

In deep-space exploration missions, deep space tracking, telemetry and command (TT&C) network in a core position. In order to guarantee its work reliability, there is a large amount of redundancy in the network. But unnecessary equipment redundancy instead of increasing the reliability of the network, which makes the network structure more complicated, increases the maintenance costs, and cause the waste of resources. In this paper, a research method for survivability of deep space tracking, telemetry and command(TT&C) network is proposed. To reduce computing time complexity of tenacity, using simulated annealing particle swarm optimization algorithm (SAPSO). Results of experiments conducted in two basic networks and one realistic network illustrate that the algorithm is impactful and high-performance to calculate network tenacity, and give the reasonable optimization suggestions of network structure design in this paper.