Sangam: A Confluence of Knowledge Streams

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

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dc.creator Dahal, Keshav P.
dc.creator Burt, G.M.
dc.creator McDonald, J.R.
dc.creator Galloway, S.J.
dc.date 2008-12-02T16:12:57Z
dc.date 2008-12-02T16:12:57Z
dc.date 2000
dc.identifier Dahal, K.P., Burt, G.M., McDonald, J.R. and Galloway, S.J. (2000). GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems. Congress on Evolutionary Computation (CEC). La Jolla, CA, USA. 16-19 July 2000. Proceedings of the Congress on Evolutionary Computation. Vol. 1., pp. 567-574.
dc.identifier http://hdl.handle.net/10454/952
dc.description Yes
dc.description 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
dc.language en
dc.relation http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=870347&isnumber=18852
dc.rights © 2000 IEEE. Reprinted from Proceedings of the Congress on Evolutionary Computation - CEC. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bradford's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
dc.subject Genetic Algorithms
dc.subject Simulated Annealing
dc.subject Generator Maintenance
dc.subject Scheduling
dc.title GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems
dc.type Conference paper


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