Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper presents a novel approach to solve the constrained unit commitment problem using Selective Self-Adaptive Ant System (SSAS) for improving search performance by automatically adapting ant populations and their transition probability parameters, which cooperates with Candidate Path Management Module (CPMM) and Effective Repairing Heuristic Module (ERHM) in reducing search space and recovering a feasible optimality region so that a high quality solution can be acquired in a very early iterative. The proposed SSAS algorithm not only enhances the convergence of search process, but also provides a suitable number of the population sharing which conducts a good guidance for trading-off between the importance of the visibility and the pheromone trail intensity. The proposed method has been performed on a test system up to 100 generating units with a scheduling time horizon of 24 hours. The numerical results show the most economical saving in the total operating cost when compared to the previous literature results. Moreover, the proposed SSAS topology can remarkably speed up the computation time of ant system algorithms, which is favorable for a large-scale unit commitment problem implementation.