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The benefits of co-evolutionary Genetic Algorithms in voyage optimisation

The benefits of co-evolutionary Genetic Algorithms in voyage optimisation
The benefits of co-evolutionary Genetic Algorithms in voyage optimisation
Reducing emissions is of increasing global importance. Within shipping, the International Maritime Organization's regulations are putting pressure on companies to quickly reduce emissions. One solution is the optimisation of a ship's route where even comparatively small reductions, in the order of 5%, provide sizeable cost and environmental benefits. The most recent advances from the Evolutionary Computation field have not been benchmarked on this problem, especially the co-evolutionary algorithms that provide the widest diversity of search. This paper compares state-of-the-art algorithms on three case studies, to show the impact of algorithm selection on the fuel consumption and expected voyage time. Four state-of-the-art Genetic Algorithms are selected to represent the leading families of Genetic Algorithm. The co-evolutionary approaches are shown to have the top performance, with cMLSGA (co-evolutionary Multi-Level Selection Genetic Algorithm) showing top performance on all the problems with the greatest potential reductions in fuel usage, 7.6% on average over the state of the art, and voyage times, 8.4% on average over the state of the art.

Genetic Algorithm, Maritime transport, Ship weather routing, Speed optimisation, Voyage optimisation
0029-8018
110261
Khan, Saima
2e3f5e83-8502-4dbf-8181-02068c399faa
Grudniewski, Przemyslaw
31ca5517-c2c8-49dd-9536-6af3aefd8d33
Muhammad, Yousaf
ac5bb916-2432-42a6-9524-b940eceb6e35
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Khan, Saima
2e3f5e83-8502-4dbf-8181-02068c399faa
Grudniewski, Przemyslaw
31ca5517-c2c8-49dd-9536-6af3aefd8d33
Muhammad, Yousaf
ac5bb916-2432-42a6-9524-b940eceb6e35
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28

Khan, Saima, Grudniewski, Przemyslaw, Muhammad, Yousaf and Sobey, Adam (2022) The benefits of co-evolutionary Genetic Algorithms in voyage optimisation. Ocean Engineering, 245, 110261, [110261].

Record type: Article

Abstract

Reducing emissions is of increasing global importance. Within shipping, the International Maritime Organization's regulations are putting pressure on companies to quickly reduce emissions. One solution is the optimisation of a ship's route where even comparatively small reductions, in the order of 5%, provide sizeable cost and environmental benefits. The most recent advances from the Evolutionary Computation field have not been benchmarked on this problem, especially the co-evolutionary algorithms that provide the widest diversity of search. This paper compares state-of-the-art algorithms on three case studies, to show the impact of algorithm selection on the fuel consumption and expected voyage time. Four state-of-the-art Genetic Algorithms are selected to represent the leading families of Genetic Algorithm. The co-evolutionary approaches are shown to have the top performance, with cMLSGA (co-evolutionary Multi-Level Selection Genetic Algorithm) showing top performance on all the problems with the greatest potential reductions in fuel usage, 7.6% on average over the state of the art, and voyage times, 8.4% on average over the state of the art.

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More information

Accepted/In Press date: 24 November 2021
Published date: 1 February 2022
Keywords: Genetic Algorithm, Maritime transport, Ship weather routing, Speed optimisation, Voyage optimisation

Identifiers

Local EPrints ID: 452835
URI: http://eprints.soton.ac.uk/id/eprint/452835
ISSN: 0029-8018
PURE UUID: 58a71a00-bcf4-43d1-9c88-d16c53bdb8fe
ORCID for Przemyslaw Grudniewski: ORCID iD orcid.org/0000-0003-0635-3125
ORCID for Adam Sobey: ORCID iD orcid.org/0000-0001-6880-8338

Catalogue record

Date deposited: 21 Dec 2021 17:51
Last modified: 02 Feb 2022 02:55

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Contributors

Author: Saima Khan
Author: Przemyslaw Grudniewski ORCID iD
Author: Yousaf Muhammad
Author: Adam Sobey ORCID iD

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