README.md

# 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 9

- You can use `Template.ipynb` to start writing the part you need to submit (about meta-heuristics).
- I propose you work as follows (You don't have to follow this though!):
    * Ensure the whole group members are familiar with the TSP problem, 2-opt and 3-opt local search techniques. (see Wikipedia article link below.)
    * Implement 2-opt or/and 3-opt.
    * Decide which meta-heuristics you want to try. Watch the guest lecture videos on Aula and check the literature related to TSP and the meta-heuristics you are thinking of.
    * Split the group into 2 sub-groups, each to work on one meta-heuristic.
    * One group member will oversee both groups' work and will be reponsible for merging the 2 notebooks into one coherent notebook.
- You may try Google Colab and/or Microsoft Azure if that helps you work better, but please be aware that I am not sure about their GDPR compliance.

I should emphasise here that "exhaustive search" and "greedy" are **not* meta-heuristics, nor are 2-opt and 3-opt. Ensure this is clear to you.

## Lab 5

- Ensure you have **Jupyter**.
	- Either install [Jupyter](https://jupyter.org/install) alone or [Anaconda](https://www.anaconda.com/distribution).
- 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):

	- [Introducing Jupyter](https://www.linkedin.com/learning/introducing-jupyter/present-data-like-a-pro-with-jupyter)
	- [Get Ready for Your Coding Interview](https://www.linkedin.com/learning/get-ready-for-your-coding-interview/welcome)
	- [Python for Data Visualization](https://www.linkedin.com/learning/python-for-data-visualization/setting-marker-type-and-colors)
	- [Python: Programming Efficiently](https://www.linkedin.com/learning/python-programming-efficiently/time-profiling)
	- [Python Statistics Essential Training](https://www.linkedin.com/learning/python-statistics-essential-training/the-power-of-visualization)

- Load and study `Investigating TSP.ipynb`.
	- Can you improve any of the functions to make them more 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](https://en.wikipedia.org/wiki/Travelling_salesman_problem). Pay attention to th **Computing a solution** section, and especially to the `2-opt` and `3-opt` techniques for defining 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 _(x<sub>1</sub>,y<sub>1</sub>)_ and _(x<sub>2</sub>,y<sub>2</sub>)_ is _sqrt[(x<sub>1</sub>-x<sub>2</sub>)<sup>2</sup>+(y<sub>1</sub>-y<sub>2</sub>)<sup>2</sup>]_.
	- **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*.



## Bibliography

- Applegate, DL, Bixby, RE, Chvátal, V, Cook, WJ, 2007, [The Traveling Salesman Problem: A Computational Study](https://locate.coventry.ac.uk/permalink/f/gr8698/COV_ALMA5199622620002011), Princeton University Press, Princeton.
- Cook, WJ 2012, [In Pursuit of the Traveling Salesman: Mathematics at the Limit of Computation](https://locate.coventry.ac.uk/permalink/f/gr8698/COV_ALMA5199665280002011), Princeton University Press, Princeton.
- Glover, F, & Kochenberger, GA (eds) 2002, [Handbook of Metaheuristics](https://locate.coventry.ac.uk/permalink/f/gr8698/COV_ALMA51109755880002011), Kluwer Academic Publishers, Secaucus.
- Gutin, G, & Punnen, AP (eds) 2002, [The Traveling Salesman Problem and Its Variations](https://locate.coventry.ac.uk/permalink/f/gr8698/COV_ALMA51125059450002011), Springer, New York, NY.
- Pintea, C.-M., 2014. [Advances in Bio-inspired Computing for Combinatorial Optimization Problems](https://locate.coventry.ac.uk/permalink/f/1r06c36/COV_ALMA5155140430002011). 1st ed. 2014.
- Steven, SS 2008, [The Algorithm Design Manual](https://locate.coventry.ac.uk/permalink/f/gr8698/COV_ALMA5190160580002011), Springer, England.
	- You may also find its [companion website](http://algorist.com/problems/Traveling_Salesman_Problem.html) useful.
- Talbi, E.-G., 2009. [Metaheuristics from design to implementation](https://locate.coventry.ac.uk/permalink/f/gr8698/COV_ALMA51117060170002011), Hoboken, NJ: John Wiley & Sons.