A novel selection-learning algorithm for multi-satellite scheduling problems

In this paper, a novel selection-learning algorithm is proposed to solve multi-satellite scheduling problems, which are proved to be equivalent to maximum independent set problems. Based on prior evolutionary algorithms, a selection operator is designed to assign each individual in the group with cognitive ability, resulting in a higher tendency for an individual to select information that are useful to its growth, thereby decreasing waste searches. Extensive simulations are performed, and the results show that the proposed algorithm works better than ants colony systems on benchmark problems.