A GENERALIZED EXTREMAL OPTIMIZATION-INSPIRED ALGORITHM FOR PREDICTIVE MAINTENANCE SCHEDULING PROBLEMS

被引：0
作者：
Arpaia, Pasquale [1 ]
Maisto, Domenico [2 ]
Manna, Carlo [3 ]
机构：
[1] Univ Sannio, Dipartimento Ingn, CERN European Lab Nucl Res, Dept Technol,Grp Magnets Superconductors & Cryost, M26220,30-03-030, CH-1211 Geneva 23, Switzerland
[2] Italian Natl Res Council CNR, Inst High Performance Comp & Networking ICAR, I-80131 Naples, Italy
[3] Univ Naples Federico II, Dipartimento Ingn Elettr, I-80125 Naples, Italy
来源：
ICEC 2010: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION | 2010年
关键词：
Artificial Intelligence; Optimization Methods; Maintenance; Scheduling; PROGNOSTICS; CRITICALITY;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A bit-encoded heuristic evolutionary optimization algorithm inspired by the Generalized Extremal Optimization method is presented. The proposed evolutionary approach aims at optimizing a predictive maintenance scheduling problem characterized by an analytically intractable objective function. A preliminary comparison with a standard genetic algorithm on a set of high-dimension cases of the considered maintenance problem shows better performance for the proposed approach.
引用
收藏
页码：70 / 76
页数：7
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