Name
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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1102256.1102335 | GECCO |
Keywords | Field | DocType |
bas,ga,mmas,sa,ant colony optimization,heuristics,optimum parameter,power plant maintenance scheduling,sensitivity analysis | Ant colony optimization algorithms,Simulated annealing,Heuristic,Mathematical optimization,Parallel metaheuristic,Scheduling (computing),Computer science,Heuristics,Genetic algorithm,Metaheuristic | Conference |
ISBN | Citations | PageRank |
1-59593-010-8 | 1 | 0.36 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wai Kuan Foong | 1 | 7 | 0.87 |
| Holger R. Maier | 2 | 738 | 72.97 |
| Angus R. Simpson | 3 | 45 | 4.95 |