- All Implemented Interfaces:
public final class NearestCityHeuristic extends Object implements ConstructiveHeuristic<Permutation>
This class implements a nearest city constructive heuristic for the TSP for use by stochastic sampling algorithms. The nearest city heuristic prefers cities that are closest to the city most recently added to the tour. Since the stochastic sampling algorithms of the library require higher heuristic values to imply preferred choice, this heuristic is implemented as: h(j) == 1.0 / (1.0 + distance(i, j)), where h(j) is the heuristic value for city j, and i is the most recently added city. If no cities have been added yet, the heuristic simply returns 1.
Method SummaryModifier and TypeMethodDescription
final intGets the required length of complete solutions to the problem instance for which this constructive heuristic is configured.
(int n)Creates an empty Partial solution, which will be incrementally transformed into a complete solution of a specified length.Gets a reference to the instance of the optimization problem that is the subject of this heuristic.
doubleHeuristically evaluates the possible addition of an element to the end of a Partial.
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
(BaseTSP problem)Constructs a nearest city heuristic for an instance of the TSP.
problem- The TSP instance to solve.
hHeuristically evaluates the possible addition of an element to the end of a Partial. Higher evaluations imply that the element is a better choice for the next element to add. For example, if you evaluate two elements, x and y, with h, and h returns a higher value for y than for x, then this means that y is believed to be the better choice according to the heuristic. Implementations of this interface must ensure that h always returns a positive result. This is because stochastic sampling algorithms such as HBSS and VBSS assume that the constructive heuristic returns only positive values.
- Specified by:
p- The current state of the Partial
element- The element under consideration for adding to the Partial
incEval- An IncrementalEvaluation of p. This method assumes that incEval is of the same runtime type as the object returned by
- The heuristic evaluation of the hypothetical addition of element to the end of p. The higher the evaluation, the more important the heuristic believes that element should be added next. The intention is to compare the value returned with the heuristic evaluations of other elements. Individual results in isolation are not necessarily meaningful.
getProblemGets a reference to the instance of the optimization problem that is the subject of this heuristic.
createPartialCreates an empty Partial solution, which will be incrementally transformed into a complete solution of a specified length.
completeLengthpublic final int completeLength()Gets the required length of complete solutions to the problem instance for which this constructive heuristic is configured.