Ant colony optimization for power plant maintenance scheduling optimization
Date
2005
Authors
Foong, W.
Maier, H.
Simpson, A.
Editors
Beyer, H.
Advisors
Journal Title
Journal ISSN
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Type:
Conference paper
Citation
Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, 2005 / Hans-Georg Beyer et al. (eds.): pp.249-256
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 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.
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Dissertation Note
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Copyright © 2005, Association for Computing Machinery