Traveling Salesman Problem
The traveling salesman problem (TSP) is certainly one of the classical as well as most frequently analyzed representatives of combinatorial optimization problems with a lot of solution methodologies and solution manipulation operators. The problem is that a person has to visit a number of cities starting from and returning to his home city. The goal is to find a tour where every city is visited exactly once and where the travel distances become minimal.
Problem Parameters:
| Parameter | Description |
|---|---|
| BestKnownQuality | The quality of the best known solution of this TSP instance. |
| BestKnownSolution | The best known solution of this TSP instance. |
| Coordinates | The x- and y-Coordinates of the cities. |
| DistanceMatrix | The matrix which contains the distances between the cities. |
| Evaluator | TSPRoundedEuclideanPathEvaluator: The operator which should be used to evaluate TSP solutions. |
| Maximization | Set to false as the Traveling Salesman Problem is a minimization problem. |
| SolutionCreator | RandomPermutationCreator: The operator which should be used to create new TSP solutions. |
| UseDistanceMatrix | True if a distance matrix should be calculated and used for evaluation, otherwise false. |
Is there a sample/tutorial?
Yes, HeuristicLab comes with a set of pre-configured algorithms that solve the "ch130" traveling salesman problem from the TSPLIB:
TPS Operators
TSPRoundedEuclideanPathEvaluator
An operator which evaluates TSP solutions given in path representation using the rounded Euclidean distance metric.
Operator Parameters:
| Parameter | Description |
|---|---|
| Coordinates | The x- and y-Coordinates of the cities. |
| DistanceMatrix | The matrix which contains the distances between the cities. |
| TSPTour | The TSP solution given in path representation which should be evaluated. |
| TSPTourLength | The evaluated quality of the TSP solution. |
| UseDistanceMatrix | True if a distance matrix should be calculated and used for evaluation, otherwise false. |


