Cuckoo search algorithm with memory and the vibrant fault diagnosis for hydroelectric generating unit

• Published in: Engineering with computers Vol. 35; no. 2; pp. 687 - 702
• Main Authors: Cheng, Jiatang, Wang, Lei, Xiong, Yan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.04.2019; Springer Nature B.V
• Subjects: Algorithms; CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Classical Mechanics; Computer Science; Computer-Aided Engineering (CAD; Control; Diagnostic systems; Fault diagnosis; Math. Applications in Chemistry; Mathematical and Computational Engineering; Model accuracy; Neural networks; Original Article; Random walk; Search algorithms; Systems Theory; Fault diagnosis; Cuckoo search; Step size factor; Memory; Hydroelectric generating unit
• ISSN: 0177-0667; 1435-5663
• DOI: 10.1007/s00366-018-0627-1

Levy flight random walk is one of the important operators of cuckoo search (CS) algorithm, and it employs the fixed step size factor to generate new candidate solutions. In this work, the memory mechanism is introduced into CS algorithm to dynamically select the appropriate step size, which differs from many CS variants by incorporating some existing algorithms into CS framework. To investigate the effectiveness of the presented version, two well-known test suites are employed. Experimental results demonstrate that CS with memory (CSM) exhibits better optimization performance compared with other CS variants. Then, a vibration fault diagnosis model of hydroelectric generating unit (HGU) based on CSM combined with BP neural network is established. Diagnostic results show that the combined model has higher classification accuracy in tackling two diagnostic examples, and also prove the superiority of the proposed algorithm in solving practical problems.