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|Title:||Ant colony optimization for power plant maintenance scheduling optimization|
|Citation:||Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation, Washington, DC, USA, 25-29 June 2005: pp. 354-357|
|Conference Name:||Genetic and Evolutionary Computation Conference (7th : 2005 : Washington, D.C.)|
|Wai Kuan Foong, Holger R. Maier & Angus R. Simpson|
|Abstract:||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 results obtained indicate that the performance of ACO algorithms 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.|
|Keywords:||Ant colony optimization; power plant maintenance scheduling; heuristics; Max-Min Ant System; Genetic Algorithm; Simulated Annealing|
|Rights:||Copyright 2005 ACM|
|Appears in Collections:||Civil and Environmental Engineering publications|
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