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|Title:||Ant colony optimization for power plant maintenance scheduling optimization|
|Citation:||Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, 2005 / Hans-Georg Beyer et al. (eds.): pp.249-256|
|Publisher:||The Association for Computing Machinery, Inc.|
|Publisher Place:||New York|
|Conference Name:||Genetic and Evolutionary Computation Conference (7th : 2005 : Washington, D.C.)|
|Wai Kuan Foong, Holger R. Maier, Angus R. Simpson|
|Abstract:||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.|
|Rights:||Copyright © 2005, Association for Computing Machinery|
|Appears in Collections:||Civil and Environmental Engineering publications|
Environment Institute publications
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