Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/81440
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Type: Conference paper
Title: Ant colony optimization for power plant maintenance scheduling optimization
Author: Foong, W.
Maier, H.
Simpson, A.
Citation: Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation, Washington, DC, USA, 25-29 June 2005: pp. 354-357
Publisher: ACM
Issue Date: 2005
Conference Name: Genetic and Evolutionary Computation Conference (7th : 2005 : Washington, D.C.)
Statement of
Responsibility: 
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
RMID: 0030000245
DOI: 10.1145/1102256.1102335
Appears in Collections:Civil and Environmental Engineering publications

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