Optimising large scale public transport network design problems using mixed-mode parallel multi-objective evolutionary algorithms

Cooper, Ian, John, Matthew P., Lewis, Rhydian, Mumford, Christine Lesley and Olden, Andrew 2014. Optimising large scale public transport network design problems using mixed-mode parallel multi-objective evolutionary algorithms. Presented at: IEEE Congress on Evolutionary Computation, Beijing, China, 6 - 11 July 2014. Evolutionary Computation (CEC). IEEE, pp. 2841-2848. 10.1109/CEC.2014.6900362

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Abstract

In this paper we present a novel tool, using both OpenMP and MPI protocols, for optimising the efficiency of Urban Transportation Systems within a defined catchment, town or city. We build on a previously presented model which uses a Genetic Algorithm with novel genetic operators to optimise route sets and provide a transport network for a given problem set. This model is then implemented within a Parallel Multi-Objective Genetic Algorithm and demonstrated to be scalable to within the scope of real world, [city-wide], problems. This paper compares and contrasts three methods of parallel distribution of the Genetic Algorithm's computational workload: a job farming algorithm and two variations on an ‘Islands’ approach. Results are presented in the paper from both single and mixed mode strategies. The results presented are from a range of previously published academic problem sets. Additionally a real world inspired problem set is evaluated and a visualisation of the optimised output is given.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Mathematics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	IEEE
ISBN:	9781479966264
Funders:	High Perfromance Computing Wales
Last Modified:	18 Jun 2018 19:27
URI:	http://orca.cf.ac.uk/id/eprint/65125

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