Permalink
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Showing
1 changed file
with
49 additions
and
1 deletion.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number  Diff line number  Diff line change 

@@ 1 +1,49 @@  
# TSPGuidance  
# 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.  


  


 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/introducingjupyter/presentdatalikeaprowithjupyter)  
 [Get Ready for Your Coding Interview](https://www.linkedin.com/learning/getreadyforyourcodinginterview/welcome)  
 [Python for Data Visualization](https://www.linkedin.com/learning/pythonfordatavisualization/settingmarkertypeandcolors)  
 [Python: Programming Efficiently](https://www.linkedin.com/learning/pythonprogrammingefficiently/timeprofiling)  
 [Python Statistics Essential Training](https://www.linkedin.com/learning/pythonstatisticsessentialtraining/thepowerofvisualization)  


 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 `2opt` and `3opt` 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*.  


  


 You can use `Template.ipynb` to start writing the part you need to submit (about metaheuristics).  
 I propose you work as follows (You don't have to follow this though!):  
* Ensure you are familiar with the TSP problem, 2opt and 3opt local search techniques. (See this [Wikipedia article](https://en.wikipedia.org/wiki/Travelling_salesman_problem))  
* Decide which metaheuristics you want to try. Watch the [guest lecture videos](https://github.coventry.ac.uk/pages/ab3735/380CT_2022/lectures/lecture7/) and check the literature related to TSP and the metaheuristics you are thinking of.  
 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* metaheuristics, nor are 2opt and 3opt. Ensure this is clear to you.  




## 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 Bioinspired 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. 