Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

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

Dahal, K.P. and Burt, G.M. and Mcdonald, J.R. and Galloway, S.J. (2002) GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems. In: Proceedings of the 2000 Congress on Evolutionary Computation. IEEE, 567 - 574. ISBN 0-7803-6375-2

Full text not available in this repository. Request a copy from the Strathclyde author

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