Dataset for publication: "The benefits of co-evolutionary Genetic Algorithms in voyage optimisation"

Grudniewski, Przemyslaw, Sobey, Adam and Khan, Saima (2021) Dataset for publication: "The benefits of co-evolutionary Genetic Algorithms in voyage optimisation". Mendeley Data doi:10.17632/ssdbwvsrm9.2 [Dataset]

Abstract

The Dataset is separated according to the cases used within the publication: - Case 1: Dalian -> San Francisco - Case 2: Southampton -> Karachi - Case 3: New York -> Oslo Within each dataset 3 files and 1 folder are included: a) First_order_approx.csv - contains information about first order approximation according to which the mesh was built, as for each node. b) Mesh.csv - contains information about the mesh used within optimisation procedure as . c) Mesh_map.png - visualisation of Mesh.csv, where: red crosses represent ; blue crosses represent ; and purple squares represent . d) Weather_data folder - where all weather data is stored in a binary format: - Weather_grid_sizes - defines size of the respective weather data as set of long/lat and resolution (step) - Naming of the files follows format: type.resolution-date_of_start-time_of_start-time_offset.bin e.g. "hycom.0p08-20180813-t00z-003" is for "hycom" type with resolution of 0.08 degree; starting on 2018-08-13 at 00 hours (12:00 am), with 3h offset from starting date/time (so in this case it is data for 03:00 am on 13-08-2018). - "Hycom" type contains raw currents data as single precision floating point values (4-bytes) for ocean components: (U-speed, V-speed, temperature, salinity). - "Wave" type contains raw waves data as single precision floating point values (4-bytes) of wave components: (height, direction, period). - "Wind" type contains raw winds data as single precision floating point values (4-bytes) for wind components: (U-speed, V-speed).

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Contributors

Creator: Przemyslaw Grudniewski

Creator: Adam Sobey

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