Sensor network optimization of gearbox based on dependence matrix and improved discrete shuffled frog leaping algorithm

被引：4

作者：

Zhao, Zhuanzhe ^{[1
,2
]}

Xu, Qingsong ^{[3
]}

Jia, Minping ^{[1
]}

机构：

[1] Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R China

[2] Anhui Polytech Univ, Sch Mech & Automot Engn, Wuhu 241000, Peoples R China

[3] Univ Macau, Fac Sci & Technol, Dept Electromech Engn, Ave Univ, Taipa, Macau, Peoples R China

来源：

NATURAL COMPUTING | 2016年 / 15卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Improved discrete shuffled frog leaping algorithm; Crossover; Mutation; Sensor network optimization; Dependence matrix; FAULT DIAGNOSTIC OBSERVABILITY; PLACEMENT OPTIMIZATION; RELIABILITY CRITERIA; GENETIC ALGORITHMS; HEALTH MANAGEMENT; LOCATION; DESIGN; SYSTEM; DEPLOYMENT; SIMULATION;

D O I：

10.1007/s11047-015-9515-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper reports a new improved discrete shuffled frog leaping algorithm (ID-SFLA) and its application in multi-type sensor network optimization for the condition monitoring of a gearbox. A mathematical model is established to illustrate the sensor network optimization based on fault-sensor dependence matrix. The crossover and mutation operators of genetic algorithm (GA) are introduced into the update strategy of shuffled frog leaping algorithm (SFLA) and a new ID-SFLA is systematically developed. Numerical simulation results show that the ID-SFLA has an excellent global search ability and outstanding convergence performance. The ID-SFLA is applied to the sensor's optimal selection for a gearbox. In comparison with GA and discrete shuffled frog leaping algorithm (D-SFLA), the proposed ID-SFLA not only poses an effective solving method with swarm intelligent algorithm, but also provides a new quick algorithm and thought for the solution of related integer NP-hard problem.

引用

页码：653 / 664

页数：12

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