Application of Ant Colony Algorithm for Optimization of Railway Alignment

The emergence of artificial intelligence based optimization heuristic algorithms is usefulin solving many complex transportation optimization problems. The application of theseartificial intelligence based optimization methods turns out to be more appropriate thanthe classical methods in terms of both quality and the computation time; particularly,when the study area becomes large and complex with a number of parametersinvolved (such as cost components, design parameters, etc.). The advantage of thesemodern tools over the classical tools is their flexibility towards the complexity of theproblems. The Ant Colony optimization (ACO) algorithm offers many possibilities forformulating research problems which depends on the nature of the problem to besolved. This study uses two variations of the ACO algorithm to solve the railwayalignment optimization problem. In this paper, the ACO algorithm is modified to searchin the entire search space, a significant departure from traditional ACO algorithmapplication, where it is confined to search within a specified equidistance planesformed between the desired configurations. In addition, to check the capability andefficiency of the modified ACO, a comparative analysis is done using two separatescenarios, one being a hypothetical case and the other being a real world case. Theresults show that the modified application of the ACO delivers more optimal solutionsover the traditional application of the ACO, for the same experimental conditions.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 12p

Subject/Index Terms

Filing Info

  • Accession Number: 01716787
  • Record Type: Publication
  • Report/Paper Numbers: 19-02577
  • Files: TRIS, TRB
  • Created Date: Sep 17 2019 2:01PM