Future Generation Computer Systems
Scheduling Space–Ground Communications for the Air Force Satellite Control Network
Journal of Scheduling
Ants can solve the team orienteering problem
Computers and Industrial Engineering
Leap before you look: an effective strategy in an oversubscribed scheduling problem
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Understanding performance tradeoffs in algorithms for solving oversubscribed scheduling
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The deep space network scheduling problem
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Adaptive problem-solving for large-scale scheduling problems: a case study
Journal of Artificial Intelligence Research
Ant colony optimization with hill climbing for the bandwidth minimization problem
Applied Soft Computing
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ant Colony Optimizations for Resource- and Timing-Constrained Operation Scheduling
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Using the ACO algorithm for path searches in social networks
Applied Intelligence
Multi-satellite control resource scheduling based on ant colony optimization
Expert Systems with Applications: An International Journal
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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.