Generator maintenance scheduling using a genetic algorithm
Fuzzy Sets and Systems - Special issue on applications of fuzzy theory in electronic power systems
Ant algorithms for discrete optimization
Artificial Life
Future Generation Computer Systems
Ant Colony Optimization
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
Replicating multi-quality web applications using ACO and bipartite graphs
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
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In order to maintain a reliable and economic electric power supply, the maintenance of power plants is becoming increasingly important. In this paper, a formulation that enables ant colony optimization (ACO) algorithms to be applied to the power plant maintenance scheduling optimization (PPMSO) problem is developed and tested on a 21-unit case study. A heuristic formulation is introduced and its effectiveness in solving the problem is investigated. The performance of two different ACO algorithms is compared, including Best Ant System (BAS) and Max-Min Ant System (MMAS), and a detailed sensitivity analysis is conducted on the parameters controlling the searching behavior of ACO algorithms. The results obtained indicate that the performance of the two ACO algorithms investigated is significantly better than that of a number of other metaheuristics, such as genetic algorithms and simulated annealing, which have been applied to the same case study previously. In addition, use of the heuristics significantly improves algorithm performance. Also, ACO is found to have similar performance for the case study considered across an identified range of parameter values.