Ant colony optimization for power plant maintenance scheduling optimization

Authors:
Wai Kuan Foong;Holger R. Maier;Angus R. Simpson
Affiliations:
The University of Adelaide, Australia;The University of Adelaide, Australia;The University of Adelaide, Australia
Venue:
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Year:
2005

Citing 4
Cited 0

Generator maintenance scheduling using a genetic algorithm

Fuzzy Sets and Systems - Special issue on applications of fuzzy theory in electronic power systems
Ant algorithms for discrete optimization

Artificial Life
MAX-MIN Ant system

Future Generation Computer Systems
Ant colony optimization for resource-constrained project scheduling

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

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.