Ant colony optimisation for finding the optimal railroad path

Engineers have applied mathematical models to find the optimal railway path in order to minimise the total cost subject to the railway limitations. There are two main issues for this problem. First, this approach results in a complex formulation for real-life applications, mainly because there are a huge number of variables and constraints. Second, to compute the total cost, different types of data are required, such as topography, right-of-way unit cost, forbidden zones and geology. Because various administrations are often responsible for preparing these data with their own standards, there is much inconsistency in the data. This paper deals with the first issue by proposing a high-performance optimisation technique that simulates the behaviour of ants to obtain a good optimum. To show the effectiveness of this algorithm in finding a solution, a comparison with common algorithms is presented. In addition, a geographical information system database is used to obtain all the necessary data to estimate the...

[1]  Paul Schonfeld,et al.  Optimizing Rail Transit Routes with Genetic Algorithms and Geographic Information System , 2007 .

[2]  Paul Schonfeld,et al.  Prescreening and Repairing in a Genetic Algorithm for Highway Alignment Optimization , 2009, Comput. Aided Civ. Infrastructure Eng..

[3]  Xin-She Yang,et al.  A literature survey of benchmark functions for global optimisation problems , 2013, Int. J. Math. Model. Numer. Optimisation.

[4]  Paul Schonfeld,et al.  Multi-objective highway alignment optimization incorporating preference information , 2014 .

[5]  Thomas Stützle,et al.  A unified ant colony optimization algorithm for continuous optimization , 2014, Eur. J. Oper. Res..

[6]  Marco Dorigo,et al.  Ant colony optimization for continuous domains , 2008, Eur. J. Oper. Res..

[7]  Paul Schonfeld,et al.  Applicability of highway alignment optimization models , 2012 .

[8]  Marco Dorigo,et al.  The hyper-cube framework for ant colony optimization , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Jun Zhang,et al.  Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems , 2008, Journal of Computer Science and Technology.

[10]  Yousef Shafahi,et al.  A Customized Particle Swarm Method to Solve Highway Alignment Optimization Problem , 2013, Comput. Aided Civ. Infrastructure Eng..

[11]  Martin Middendorf,et al.  A Population Based Approach for ACO , 2002, EvoWorkshops.

[12]  Christian Blum,et al.  Beam-ACO - hybridizing ant colony optimization with beam search: an application to open shop scheduling , 2005, Comput. Oper. Res..

[13]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[14]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[15]  J. Dréo,et al.  Continuous interacting ant colony algorithm based on dense heterarchy , 2004, Future Gener. Comput. Syst..

[16]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[17]  Patrick Siarry,et al.  A Continuous Genetic Algorithm Designed for the Global Optimization of Multimodal Functions , 2000, J. Heuristics.

[18]  Petros Koumoutsakos,et al.  Learning probability distributions in continuous evolutionary algorithms – a comparative review , 2004, Natural Computing.

[19]  Christian Blum,et al.  Beam-ACO for Simple Assembly Line Balancing , 2008, INFORMS J. Comput..

[20]  Paul Schonfeld,et al.  Highway Alignment Optimization Through Feasible Gates , 2007, Artificial Intelligence in Highway Location and Alignment Optimization.

[21]  Ian C. Parmee,et al.  The Ant Colony Metaphor for Searching Continuous Design Spaces , 1995, Evolutionary Computing, AISB Workshop.