Travelling Salesman Problem (TSP)

Travelling Salesman Problem

This post comes from a work done over an undergraduate course, Programming Languages 2, where the Travelling Salesman Problem was studied to analyse and apply different data structures, like binary tree variations. The implementations discussed below are found at my github.

The TSP problem can be summarized as: given a set of Euclidean 2D points, the problem consist of finding the shortest possible tour, which should pass over each point just once and come back to the initial tour.

Euclidean points TSP tour

Two different algorithms, or heuristics, were used to construct the tour (the path to visit all vertices or cities):

Implementations and efficiency conjectures

Farthest Insertion

The Farthest Insertion’s heuristic consists of two basic actions:

  • searching for the farthest free vertex (one that isn’t yet in the tour) from the tour;
  • inserting the selected vertex in the tour in a way that the new tour is the shortest possible path.

The distance between a free vertex and the tour is the distance between this vertex and the closest vertex of the tour. For this, an algorithm like Nearest Neighbor can be used, selecting the free vertex whose distance from the tour is the greatest.

Suppose $C$ as the distance between two vertexes, $i$ and $j$ as vertexes already in the tour and $r$ as the free vertex selected as pointed above. The vertex $r$ must be inserted in the tour obeying the follow equation

To understand better the problem and to compare the performance of different data structures for indexing (storage of the free vertexes) and the tour, two different implementation were done: the simplest, where both free vertexes and tour were stored with simple lists (for a heap and for spacial indexing, respectively); and the fastest, where the free vertexes were stored in a B-Tree, as a priority queue, and the tour was stored in a K-d Tree, for spacial indexing.

First version — Lists

In this first implementation, both sets of vertexes, the free ones and the tour, are stored using simple lists. The pour performance of this implementation comes from the necessity of a full transversal of the lists for some operations over them.

A naive implementation would spend $O(n^2)$ to search for the farthest vertex from the tour and $O(n^3)$ to run the whole algorithm, i.e., to execute FI until all free vertexes are added to the tour. To avoid repeated calculations and to keep the cost as $O(n^2)$, when the farthest vertex is being searched, the distances between the free vertexes and the tour can be updated considering only the last inserted vertex of the tour.

Second version — B-tree and Kd-tree

In the second implementation, the free vertexes are stored in a B-tree and tour in a Kd-tree. The main advantage of using a B-tree for the free vertexes is the possibility of indexing them respectively to their distance to the tour, working like a priority queue, in an always balanced tree. This means that we can take the farthest vertex and update the tree always in $O(\log n)$, even for the worst case.

Every time we add a new vertex to the tour, creating therefore a new edge, the distance of every remaining free vertex should continue the same or be decreased. We can check this by searching for the new farthest vertex, in $O(\log n)$, and recalculating its distance to the tour; if it didn’t changed, we don’t need to worry about the tree’s indexes and no updates need to be made; otherwise, we take the vertex and inserted it again in the tree and repeat the procedure of searching the farthest vertex and updating its distance. Note that unnecessary distance updates are always avoided.

On the other hand, the Kd-tree is used as an efficient data structure to look for the best insert position in the tour. We could say that the Kd-tree organizes their vertexes different rectangles determined by the $x$ and $y$ coordinates (a visual representation is given in the figure below taken from Wikipedia). The organization of the tree favors searches involving multidimensional search keys, like the coordinates $x$ and $y$ in a 2D space. So, an insertion of a vertex by its coordinates can be done in $O(\log n)$ too.

[Visual representation of a Kd-tree structure.]
— Visual representation of a Kd-tree structure.


Like the first implementation, the main loop of the algorithm is executed until we don’t have any remaining free vertex. The efficiency difference comes from the search and insertion of the farthest vertex. The B-tree allows us to search for the farthest vertex in $O(\log n)$, since we are using a balanced tree and the distance update is done considering only the last vertex inserted in the tour. As the Kd-tree divides the 2D space in two half-spaces, we can insert a vertex using its $x$ and $y$ coordinates in $O(\log n)$. Therefore, this second implementation of the Farthest Insertion can run in $O(n \log n)$.


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Published: February 25 2014