A kind of parameters selection for ant colony algorithms(ACAs) based on graph knowledge transfer is proposed,
where all of running parameters are taken into account simultaneously. Firstly, all source tasks containing knowledge (running parameters for ACAs) are mapped onto a high-dimensional transfer space, and transfer weights are used to connect these source tasks. In this way, a model transfer graph is thus constructed. Then, the model transfer graph is extended to include a target task and a transfer function can be obtained according to a graph theory. Finally, a group of optimal parameters for the target task can be automatically determined by using a least-squares method. Simulation results involving a robot path planning problem show the intelligence, rapidness and reasonability of the proposed method.