• DocumentCode
    1791751
  • Title

    Metaheuristics in big data: An approach to railway engineering

  • Author

    Nunez, Silvia Galvan ; Attoh-Okine, Nii

  • Author_Institution
    Dept. of Civil & Environ. Eng., Univ. of Delaware, Newark, DE, USA
  • fYear
    2014
  • fDate
    27-30 Oct. 2014
  • Firstpage
    42
  • Lastpage
    47
  • Abstract
    Big data is becoming increasingly important in various fields; railway engineering is no exception. The use of advanced analysis tools will lead to improved reliability and safety in railway systems. This paper addresses how metaheuristics can be used as an optimization technique to accurately analyze large data in railway engineering. Contributions in both optimization and application in railway engineering are also mentioned. Also, future research towards data analysis in real-life problems is discussed.
  • Keywords
    Big Data; data analysis; optimisation; railway engineering; railway safety; Big Data; data analysis tools; metaheuristics; optimization technique; railway engineering; railway reliability; railway safety; Algorithm design and analysis; Big data; Conferences; Data analysis; Genetic algorithms; Optimization; Railway engineering; Big Data; Metaheuristics; Railway;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (Big Data), 2014 IEEE International Conference on
  • Conference_Location
    Washington, DC
  • Type

    conf

  • DOI
    10.1109/BigData.2014.7004430
  • Filename
    7004430
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1791751