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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.

REFERENCES

• 1: Aldridge, C. J., K. P. Dahal, and J. R. McDonald, Genetic Algorithms For Scheduling Generation And Maintenance In Power Systems, in Modern Optimisation Techniques in Power Systems, Y.-H. Song, Editor. 1999, Kluwer Academic Publishers: Dordrecht; Boston. p. 63--89.
• 2: K. P. Dahal , C. J. Aldridge , J. R. McDonald, Generator maintenance scheduling using a genetic algorithm, Fuzzy Sets and Systems, v.102 n.1, p.21-29, Feb. 16, 1999 [doi>10.1016/S0165-0114(98)00199-7]
• 3: Dahal, K. P., J. R. McDonald, and G. M. Burt, Modern Heuristic Techniques For Scheduling Generator Maintenance In Power Systems. Transactions of the Institute of Measurement and Control, 2000. 22(2): p. 179--194.
• 4: Marco Dorigo , Gianni Di Caro , Luca M. Gambardella, Ant algorithms for discrete optimization, Artificial Life, v.5 n.2, p.137-172, April 1999 [doi>10.1162/106454699568728]
• 5: Merkle, D., M. Middendorf, and H. Schmeck, Ant Colony Optimisation for Resource-Constrained Project Scheduling. IEEE Transactions on Evolutionary Computation, 2002. 6(4): p. 333--346.
• 6: Thomas Stützle , Holger H. Hoos, MAX-MIN Ant system, Future Generation Computer Systems, v.16 n.9, p.889-914, June 2000
• 7: Yamayee, Z., K. Sidenblad, and M. Yoshimura, A Computational Efficient Optimal Maintenance Scheduling Method. IEEE Transactions on Power Apparatus and Systems, 1983. PAS-102(2): p. 330--338.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Multiagent systems; Intelligent agents

General Terms:
Algorithms, Experimentation, Management, Performance

Keywords:
ant colony optimization, genetic algorithm, heuristics, max-min ant system, power plant maintenance scheduling, simulated annealing