• DocumentCode
    2973973
  • Title

    Cuckoo search for business optimization applications

  • Author

    Xin-She Yang ; Deb, Sujay ; Karamanoglu, Mehmet ; Xingshi He

  • Author_Institution
    Sch. of Sci. & Technol., Middlesex Univ., London, UK
  • fYear
    2012
  • fDate
    21-22 Nov. 2012
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Cuckoo search has become a popular and powerful metaheuristic algorithm for global optimization. In business optimization and applications, many studies have focused on support vector machine and neural networks. In this paper, we use cuckoo search to carry out optimization tasks and compare the performance of cuckoo search with support vector machine. By testing benchmarks such as project scheduling and bankruptcy predictions, we conclude that cuckoo search can perform better than support vector machine.
  • Keywords
    business data processing; neural nets; optimisation; search problems; support vector machines; bankruptcy predictions; business optimization applications; cuckoo search; global optimization; metaheuristic algorithm; neural networks; project scheduling; support vector machine; Algorithm design and analysis; Business; Optimization; Particle swarm optimization; Prediction algorithms; Search problems; Support vector machines; algorithm; cuckoo search; metaheuristics; optimization; swarm intelligence;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing and Communication Systems (NCCCS), 2012 National Conference on
  • Conference_Location
    Durgapur
  • Print_ISBN
    978-1-4673-1952-2
  • Type

    conf

  • DOI
    10.1109/NCCCS.2012.6412973
  • Filename
    6412973