GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

29 Citations (Scopus)

Abstract

Proposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problems
LanguageEnglish
Title of host publicationProceedings of the 2000 Congress on Evolutionary Computation
PublisherIEEE
Pages567 - 574
Number of pages8
Volume1
ISBN (Print)0-7803-6375-2
DOIs
Publication statusPublished - 6 Aug 2002

Fingerprint

Simulated annealing
Genetic algorithms
Scheduling
Integer programming

Keywords

  • genetic algorithms
  • simulated annealing
  • power system reliability
  • linear programming

Cite this

Dahal, K.P. ; Burt, G.M. ; Mcdonald, J.R. ; Galloway, S.J. / GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems. Proceedings of the 2000 Congress on Evolutionary Computation. Vol. 1 IEEE, 2002. pp. 567 - 574
@inproceedings{a3f8040cefcd45e0916967794a9fe9cb,
title = "GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems",
abstract = "Proposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problems",
keywords = "genetic algorithms , simulated annealing, power system reliability, linear programming",
author = "K.P. Dahal and G.M. Burt and J.R. Mcdonald and S.J. Galloway",
year = "2002",
month = "8",
day = "6",
doi = "10.1109/CEC.2000.870347",
language = "English",
isbn = "0-7803-6375-2",
volume = "1",
pages = "567 -- 574",
booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation",
publisher = "IEEE",

}

Dahal, KP, Burt, GM, Mcdonald, JR & Galloway, SJ 2002, GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems. in Proceedings of the 2000 Congress on Evolutionary Computation. vol. 1, IEEE, pp. 567 - 574. https://doi.org/10.1109/CEC.2000.870347

GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems. / Dahal, K.P.; Burt, G.M.; Mcdonald, J.R.; Galloway, S.J.

Proceedings of the 2000 Congress on Evolutionary Computation. Vol. 1 IEEE, 2002. p. 567 - 574.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems

AU - Dahal, K.P.

AU - Burt, G.M.

AU - Mcdonald, J.R.

AU - Galloway, S.J.

PY - 2002/8/6

Y1 - 2002/8/6

N2 - Proposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problems

AB - Proposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problems

KW - genetic algorithms

KW - simulated annealing

KW - power system reliability

KW - linear programming

U2 - 10.1109/CEC.2000.870347

DO - 10.1109/CEC.2000.870347

M3 - Conference contribution book

SN - 0-7803-6375-2

VL - 1

SP - 567

EP - 574

BT - Proceedings of the 2000 Congress on Evolutionary Computation

PB - IEEE

ER -

Dahal KP, Burt GM, Mcdonald JR, Galloway SJ. GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems. In Proceedings of the 2000 Congress on Evolutionary Computation. Vol. 1. IEEE. 2002. p. 567 - 574 https://doi.org/10.1109/CEC.2000.870347