Cooper, Ian  ORCID: https://orcid.org/0000-0002-4415-7988, John, Matthew P., Lewis, Rhydian ORCID: https://orcid.org/0000-0003-1046-811X, Mumford, Christine Lesley ORCID: https://orcid.org/0000-0002-4514-0272 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).
Proceedings of the 2014 IEEE Congress on Evolutionary Computation.
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: | Schools > Computer Science & Informatics Schools > Mathematics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Publisher: | IEEE |
| ISBN: | 9781479966264 |
| Funders: | High Perfromance Computing Wales |
| Date of First Compliant Deposit: | 30 March 2016 |
| Last Modified: | 08 Jan 2025 12:00 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/65125 |
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