This is the third part in my series on the "travelling salesman problem" (TSP). Part one covered defining the TSP and utility code that will be used for the various optimisation algorithms I shall discuss. Part two covered "hill-climbing" (the simplest stochastic optimisation method).
So after implementing hill-climbing, I thought it would be a worthwhile exercise to use Python's profile module and see what the slow parts of the code were. To do this I set-up a basic hill-climb on the 100 city data set and then called the run method of profile:
import …
This is the second part in my series on the "travelling salesman problem" (TSP). Part one covered defining the TSP and utility code that will be used for the various optimisation algorithms I shall discuss.
This is the first part in my series on the “travelling salesman problem” (TSP). An outline of what I plan to cover can be seen in the prologue.
To kick things off here's a quick quote:
The traveling salesman problem (TSP) asks for the shortest route to visit a collection …
The Travelling Salesman Problem (TSP) is a classic combinatorial optimisation problem. It also happens to be a problem I have spent various parts of my life looking at.
At my first job out of uni we worked on solving real-world problems that usually involved the TSP in some fashion. Often …