Class CombinatoricsProblem
- java.lang.Object
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- java.util.AbstractCollection<E>
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- java.util.AbstractSet<E>
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- java.util.HashSet<OptProbElement>
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- org.tweetyproject.math.opt.problem.GeneralConstraintSatisfactionProblem
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- org.tweetyproject.math.opt.problem.CombinatoricsProblem
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,java.lang.Iterable<OptProbElement>
,java.util.Collection<OptProbElement>
,java.util.Set<OptProbElement>
- Direct Known Subclasses:
KnapSack
,TravelingSalesman
public abstract class CombinatoricsProblem extends GeneralConstraintSatisfactionProblem
This class implements a combinatorial optimization problem- Author:
- Sebastian Franke
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description java.util.List<ElementOfCombinatoricsProb>
elements
static int
MAXIMIZE
Static constant for the type "maximization"static int
MINIMIZE
Static constant for the type "minimization"
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Constructor Summary
Constructors Constructor Description CombinatoricsProblem(java.util.List<ElementOfCombinatoricsProb> elements, int[][] graphRepresantation)
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Method Summary
Modifier and Type Method Description java.util.ArrayList<ElementOfCombinatoricsProb>
createDifference(java.util.ArrayList<ElementOfCombinatoricsProb> c)
abstract java.util.ArrayList<ElementOfCombinatoricsProb>
createRandomNewSolution(java.util.ArrayList<ElementOfCombinatoricsProb> currSol)
create a solution that changes the solution currSol a little bit (i.e.: for TSP: swap 2 cities; for KnapSack: add a random element) for currSol == null: create a random solutionabstract double
evaluate(java.util.ArrayList<ElementOfCombinatoricsProb> sol)
evaluates the solutionjava.util.ArrayList<java.util.ArrayList<ElementOfCombinatoricsProb>>
formNeighborhood(java.util.ArrayList<ElementOfCombinatoricsProb> currSol, int minIterations, int maxIteration, double threshold)
int[][]
getGraphrepresentation()
abstract java.lang.Double
getHeuristicValue(ElementOfCombinatoricsProb solutionComponent, java.lang.Integer getCurrentIndex, ElementOfCombinatoricsProb initialReference, ElementOfCombinatoricsProb[] sol)
for Ant optimization: give a chance between 0 and 1 for accepting solutionComponent to the solution solabstract double[][]
getRepresentation()
for Ant optimization: represent the problem as an adjacence matrixabstract boolean
isValid(java.util.ArrayList<ElementOfCombinatoricsProb> sol)
checks if a given solution is valid under all constraintsabstract double
sumOfWeights(java.util.ArrayList<ElementOfCombinatoricsProb> sol)
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Methods inherited from class java.util.HashSet
add, clear, clone, contains, isEmpty, iterator, remove, size, spliterator
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Field Detail
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MINIMIZE
public static final int MINIMIZE
Static constant for the type "minimization"- See Also:
- Constant Field Values
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MAXIMIZE
public static final int MAXIMIZE
Static constant for the type "maximization"- See Also:
- Constant Field Values
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elements
public java.util.List<ElementOfCombinatoricsProb> elements
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Constructor Detail
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CombinatoricsProblem
public CombinatoricsProblem(java.util.List<ElementOfCombinatoricsProb> elements, int[][] graphRepresantation)
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Method Detail
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createDifference
public java.util.ArrayList<ElementOfCombinatoricsProb> createDifference(java.util.ArrayList<ElementOfCombinatoricsProb> c)
- Parameters:
c
- the List to be subtracted from "this" List- Returns:
- the differnece of the lists
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sumOfWeights
public abstract double sumOfWeights(java.util.ArrayList<ElementOfCombinatoricsProb> sol)
- Parameters:
sol
- is the solution to be viewd- Returns:
- if the solution sol is valid under the constraints of the problem
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formNeighborhood
public java.util.ArrayList<java.util.ArrayList<ElementOfCombinatoricsProb>> formNeighborhood(java.util.ArrayList<ElementOfCombinatoricsProb> currSol, int minIterations, int maxIteration, double threshold)
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getGraphrepresentation
public int[][] getGraphrepresentation()
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createRandomNewSolution
public abstract java.util.ArrayList<ElementOfCombinatoricsProb> createRandomNewSolution(java.util.ArrayList<ElementOfCombinatoricsProb> currSol)
create a solution that changes the solution currSol a little bit (i.e.: for TSP: swap 2 cities; for KnapSack: add a random element) for currSol == null: create a random solution- Parameters:
currSol
- the current solution- Returns:
- the solution
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evaluate
public abstract double evaluate(java.util.ArrayList<ElementOfCombinatoricsProb> sol)
evaluates the solution- Parameters:
sol
- some solution- Returns:
- the target function for sol
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isValid
public abstract boolean isValid(java.util.ArrayList<ElementOfCombinatoricsProb> sol)
checks if a given solution is valid under all constraints- Parameters:
sol
- some solution- Returns:
- true iff the solution is valid
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getHeuristicValue
public abstract java.lang.Double getHeuristicValue(ElementOfCombinatoricsProb solutionComponent, java.lang.Integer getCurrentIndex, ElementOfCombinatoricsProb initialReference, ElementOfCombinatoricsProb[] sol)
for Ant optimization: give a chance between 0 and 1 for accepting solutionComponent to the solution sol- Parameters:
solutionComponent
- : Element to be checked *getCurrentIndex
- : the length of the solutioninitialReference
- : default starting point for the solution (the first Element in the solution)sol
- : array representation of a solution (needed for Ant optimization)- Returns:
- the heuristic value
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getRepresentation
public abstract double[][] getRepresentation()
for Ant optimization: represent the problem as an adjacence matrix
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