Please use this identifier to cite or link to this item: https://doi.org/10.1109/ISIE.2011.5984204
Title: Supervisory evolutionary optimization strategy for adaptive maintenance schedules
Authors: Wang, Z.
Chang, C.S. 
Keywords: Adaptive Maintenance Advisor
Offshore Power System
Supervisory Evolutionary Optimization Strategy
Supervisory Rules
System Maintenance Optimizer
Issue Date: 2011
Citation: Wang, Z.,Chang, C.S. (2011). Supervisory evolutionary optimization strategy for adaptive maintenance schedules. Proceedings - ISIE 2011: 2011 IEEE International Symposium on Industrial Electronics : 1137-1142. ScholarBank@NUS Repository. https://doi.org/10.1109/ISIE.2011.5984204
Abstract: A supervisory strategy is proposed for improving the performance of an evolutionary-algorithm-based system-maintenance optimizer developed in our previous work for offshore power systems. The system-maintenance optimizer generates a set of initial maintenance plans, and exports them to an intelligent maintenance advisor connected to it for implementation. The proposed supervisory strategy uses a set of intelligent rules for adjusting the crossover and mutation rates of the present evolutionary algorithm. A mechanism is developed for refining and generalizing the supervisory rules according to the user's experience. The proposed supervisory strategy aims to improve the search ability and efficiency of the present evolutionary algorithm. Merits of the proposed supervisory strategy are demonstrated in case studies using our system-maintenance optimizer. © 2011 IEEE.
Source Title: Proceedings - ISIE 2011: 2011 IEEE International Symposium on Industrial Electronics
URI: http://scholarbank.nus.edu.sg/handle/10635/71903
ISBN: 9781424493128
DOI: 10.1109/ISIE.2011.5984204
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