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Guide for the 380CT Assignment on TSP

The actual part you need to submit is the Metaheuristics section. The rest is meant to introduce you to the basics.

Lab 5

  • Ensure you have Jupyter, and load Investigating TSP.ipynb.

    • Either launch Anaconda through AppsAnywhere, or if you are working on your machine then I recommend installing Anaconda.
  • Familiarise yourself with Jupyter functionaility. Consider taking LinkedIn Learning courses (free through the university) or any suitable alternatives. Here is a recommended set (e.g. each member of the group takes one):

  • Study Investigating TSP.ipynb.

    • Can you improve any of the functions implemented in Investigating TSP.ipynb to make them mor efficient?
    • See how large you can make $n$ while testing exhaustive_search().
    • Check that greedy_nearest_neighbours() is correct. If not then fix it!
  • Read the Wikipedia article on TSP. Pay attention to th Computing a solution section, and especially to the 2-opt and 3-opt techniques for defininf neighbourhoods.

  • Experiment with generating your own graph families. For example:

    • Euclidean graphs: generate points using (x,y) coordinates, then generate the adjacency matrix by calculating all the required distances. Recall that the distance between two points (x1,y1) and (x2,y2) is sqrt[(x1-x2)2+(y1-y2)2].
    • Graphs with obvious shortest cycle: think of a graph where all the distances are 2 except for the edges on a predefined cycle, where the distance is 1. Such a graph would be useful for testing/debugging the nearest neighbours greedy search.

Graph ideas...

Whiteboard from a tutorial session:

Image of Yaktocat



Guide for the 380CT Assignment on TSP






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