Learnable ant colony optimization algorithm for solving satellite ground station scheduling problems

With the increased observing requirements, more and more satellites and ground stations are joined to the earth observing system. It is urgent to effectively allocate the satellite ground station resources using some scientific techniques. Aiming to the satellite ground station scheduling problem, a learnable ant colony optimization (LACO) algorithm is proposed. Experimental results show that LACO is a viable and effective approach for the satellite ground station scheduling problem. This approach legitimately combines the ant colony optimization model with the knowledge model, which largely pursues the integrating advantages of these models. The proposed approach provides a useful reference to the improvement of existing optimization approaches.