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

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: Computer Science & Informatics
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: 27 Oct 2022 09:17
URI: https://orca.cardiff.ac.uk/id/eprint/65125

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