The Learnable Ant Colony Optimization to Satellite Ground Station System Scheduling Problems

The Learnable Ant Colony Optimization (LACO) is proposed to satellite ground station system scheduling problems. The LACO employs an integrated modelling idea which combines the ant colony model with the knowledge model. In order to improve the performance, LACO largely pursues the complementary advantages of ant colony model and knowledge model. Experimental results suggest that LACO is a feasible and effective approach for the satellite ground station system scheduling problem.

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Zaproponowanie wykorzystanie algorytmu LACO (Learnable Ant Colony Optimization) do rozwiązywania problemu planowania działań naziemnej stacji satelitarnej.