• DocumentCode
    1851247
  • Title

    Let the Ants Deploy Your Software - An ACO Based Deployment Optimisation Strategy

  • Author

    Aleti, Aldeida ; Grunske, Lars ; Meedeniya, Indika ; Moser, Irene

  • Author_Institution
    Fac. of ICT, Swinburne Univ. of Technol., Hawthorn, VIC, Australia
  • fYear
    2009
  • fDate
    16-20 Nov. 2009
  • Firstpage
    505
  • Lastpage
    509
  • Abstract
    Decisions regarding the mapping of software components to hardware nodes affect the quality of the resulting system. Making these decisions is hard when considering the ever-growing complexity of the search space, as well as conflicting objectives and constraints. An automation of the solution space exploration would help not only to make better decisions but also to reduce the time of this process. In this paper, we propose to employ Ant Colony Optmisation (ACO) as a multi-objective optimisation strategy. The constructive approach is compared to an iterative optimisation procedure - a Genetic Algorithm (GA) adaptation - and was observed to perform suprisingly similar, although not quite on a par with the GA, when validated based on a series of experiments.
  • Keywords
    genetic algorithms; ant colony optmisation; deployment optimisation strategy; genetic algorithm adaptation; multi-objective optimisation; solution space exploration; Ant colony optimization; Design engineering; Genetic algorithms; Hardware; Iterative methods; Reliability engineering; Safety; Software algorithms; Software quality; Space exploration; Ant Colony Optimisation; Component Deployment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Automated Software Engineering, 2009. ASE '09. 24th IEEE/ACM International Conference on
  • Conference_Location
    Auckland
  • ISSN
    1938-4300
  • Print_ISBN
    978-1-4244-5259-0
  • Electronic_ISBN
    1938-4300
  • Type

    conf

  • DOI
    10.1109/ASE.2009.59
  • Filename
    5431744