Skip to main navigation Skip to search Skip to main content

A method of test points optimization selection based on improved bacterial foraging algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, an optimal selection method of the test points in the field of test-Ability design is provided. Based on bacterial foraging algorithm and improved particle swarm optimization algorithm. This proposed method, which is named SC BFO (Swarm Cooperation Bacteria Foraging Optimization), is applied in optimization selection of test points. Firstly, in order to ensure the availability of the algorithm, the reliability, accuracy and robustness of the algorithm are tested by using the classical test function. Subsequently, the optimization algorithm is used in a case of test-Ability design and the simulation results show that FDR (Fault Detection Rate) and FIR (Fault Isolation Rate) are both improved as well as the cost of test is reduced by using the SC BFO algorithm.

Original languageEnglish
Title of host publicationProceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016
EditorsQiang Miao, Zhaojun Li, Ming J. Zuo, Liudong Xing, Zhigang Tian
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509027781
DOIs
StatePublished - 16 Jan 2017
Event7th IEEE Prognostics and System Health Management Conference, PHM-Chengdu 2016 - Chengdu, Sichuan, China
Duration: 19 Oct 201621 Oct 2016

Publication series

NameProceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016

Conference

Conference7th IEEE Prognostics and System Health Management Conference, PHM-Chengdu 2016
Country/TerritoryChina
CityChengdu, Sichuan
Period19/10/1621/10/16

Keywords

  • Improved bacterial foraging algorithm
  • Optimization problem
  • Test point selection
  • Test-Ability design

Fingerprint

Dive into the research topics of 'A method of test points optimization selection based on improved bacterial foraging algorithm'. Together they form a unique fingerprint.

Cite this