Guidance-solution based ant colony optimization for satellite control resource scheduling problem

An ant colony optimization (ACO) approach for the satellite control resource scheduling problem is presented. Based on the observation that the solution space of the problem is sparse, this ACO approach is combined with a guidance solution based pheromone updating method to avoid trapping in local optima. The basic idea of this method is to change the distribution of pheromone trails by updating them with a guidance solution once the algorithm stagnates. We compare the proposed algorithm with several other heuristics. The experimental results demonstrate that our approach possesses strong competitive advantage in exploring global best solutions.