Ant colony optimization for power plant maintenance scheduling optimization
Date
2005
Authors
Foong, W.
Maier, H.
Simpson, A.
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Conference paper
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Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation, Washington, DC, USA, 25-29 June 2005: pp. 354-357
Statement of Responsibility
Wai Kuan Foong, Holger R. Maier & Angus R. Simpson
Conference Name
Genetic and Evolutionary Computation Conference (7th : 2005 : Washington, D.C.)
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.
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Copyright 2005 ACM