Abstract: The essence of measurement node selection problem in SDN is solving the minimum vertex cover model.However,protection for the traffic routing information leads to the failure of flow-based dynamic measurement node selection scheme,having to choose the static measurement node selection.Available static measurement node selection algorithms have the problems of low measurement accuracy and long running time.To address the problem of poor performance of measurement node selection algo-rithm in SDN,in this paper a measurement node selection scheme based on genetic and ant colony algorithms is proposed,where a G-ACO algorithm is designed to dynamically integrate the genetic algorithm and the ant colony algorithms to solve the minimum vertex cover model and to effectively improve the solving speed.Finally,the proposed scheme is verified in four network topologies with different orders of magnitude in SDN simulation network environment.The G-ACO genetic ant colony optimization algo-rithm outperforms other algorithms,and has higher optimization searching ability and algorithm stability.