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Addressing issue idaholab#2427: making the crowding distance of the r…
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…epeaqted boundry points on the nondominated fronts infinity
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@Jimmy-INL
Jimmy-INL committed Jan 18, 2025
1 parent 2022286 commit 71e36ec
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Showing 7 changed files with 264 additions and 255 deletions.
22 changes: 16 additions & 6 deletions ravenframework/utils/frontUtils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,7 +114,7 @@ def crowdingDistance(rank, popSize, fitness):
@ In, fitness, np.array, matrix contains fitness values for each element of the population
@ Out, crowdDist, np.array, array of crowding distances
"""
if isinstance(rank,xr.DataArray):
if isinstance(rank, xr.DataArray):
rank = rank.data
crowdDist = np.zeros(popSize)
fronts = np.unique(rank)
Expand All @@ -126,7 +126,6 @@ def crowdingDistance(rank, popSize, fitness):
frontIndices[r].append(i)

for f in fronts:
# front = np.where(rank == f)[0] # Get indices of current front
front = frontIndices[f] # Get indices of current front
numObjectives = fitness.shape[1]
numPoints = len(front)
Expand All @@ -143,6 +142,16 @@ def crowdingDistance(rank, popSize, fitness):
crowdDist[sortedFront[0]] = np.inf
crowdDist[sortedFront[-1]] = np.inf

# Ensure all repeated boundary points are set to infinity
boundaryValueMin = fitness[sortedFront[0], obj]
boundaryValueMax = fitness[sortedFront[-1], obj]

for i in range(1, numPoints - 1):
if fitness[sortedFront[i], obj] == boundaryValueMin:
crowdDist[sortedFront[i]] = np.inf
if fitness[sortedFront[i], obj] == boundaryValueMax:
crowdDist[sortedFront[i]] = np.inf

# Skip normalization if all values are identical
fMax = fitness[sortedFront, obj].max()
fMin = fitness[sortedFront, obj].min()
Expand All @@ -151,7 +160,8 @@ def crowdingDistance(rank, popSize, fitness):

# Calculate normalized distances with epsilon protection
for i in range(1, numPoints - 1):
nextObjValue = fitness[sortedFront[i + 1], obj]
prevObjValue = fitness[sortedFront[i - 1], obj]
crowdDist[sortedFront[i]] += (nextObjValue - prevObjValue) / (fMax - fMin)
return crowdDist
if crowdDist[sortedFront[i]] != np.inf:
nextObjValue = fitness[sortedFront[i + 1], obj]
prevObjValue = fitness[sortedFront[i - 1], obj]
crowdDist[sortedFront[i]] += (nextObjValue - prevObjValue) / (fMax - fMin)
return crowdDist
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435 changes: 217 additions & 218 deletions ...izers/GeneticAlgorithms/gold/discrete/constrained/Multi_MinwoReplacement/opt_export_0.csv
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62 changes: 31 additions & 31 deletions ...eticAlgorithms/gold/discrete/constrained/MultiwoReplacementFeasibleFirst/opt_export_0.csv
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
x1,x2,x3,obj1,obj2,age,batchId,rank,CD,FitnessEvaluation_obj1,FitnessEvaluation_obj2,accepted
3.0,2.0,4.0,19.0,7.0,0.0,1.0,3.0,inf,-19.0,7.0,first
3.0,2.0,4.0,19.0,7.0,0.0,1.0,3.0,0.0,-19.0,7.0,first
3.0,2.0,4.0,19.0,7.0,0.0,1.0,3.0,inf,-19.0,7.0,first
3.0,4.0,2.0,17.0,11.0,0.0,1.0,1.0,inf,-17.0,11.0,first
2.0,3.0,4.0,20.0,8.0,0.0,1.0,3.0,inf,-20.0,8.0,first
4.0,2.0,3.0,17.0,8.0,0.0,1.0,2.0,inf,-17.0,8.0,first
Expand All @@ -12,42 +12,42 @@ x1,x2,x3,obj1,obj2,age,batchId,rank,CD,FitnessEvaluation_obj1,FitnessEvaluation_
4.0,3.0,2.0,16.0,10.0,1.0,2.0,1.0,inf,-17.0,11.0,accepted
2.0,3.0,4.0,20.0,8.0,1.0,2.0,1.0,inf,-17.0,11.0,accepted
4.0,3.0,2.0,16.0,10.0,1.0,2.0,1.0,inf,-16.0,10.0,accepted
3.0,2.0,4.0,19.0,7.0,1.0,2.0,1.0,0.0,-16.0,10.0,accepted
3.0,2.0,4.0,19.0,7.0,1.0,2.0,1.0,inf,-16.0,10.0,accepted
4.0,3.0,2.0,16.0,10.0,1.0,2.0,1.0,inf,-16.0,10.0,accepted
3.0,2.0,4.0,19.0,7.0,1.0,2.0,2.0,inf,-17.0,8.0,accepted
2.0,3.0,4.0,20.0,8.0,1.0,2.0,2.0,inf,-19.0,10.0,accepted
3.0,2.0,4.0,19.0,7.0,1.0,2.0,2.0,inf,-19.0,10.0,accepted
2.0,3.0,4.0,20.0,8.0,1.0,2.0,3.0,inf,-19.0,7.0,accepted
3.0,2.0,4.0,19.0,7.0,1.0,2.0,3.0,inf,-20.0,8.0,accepted
3.0,2.0,4.0,19.0,7.0,1.0,2.0,3.0,inf,-19.0,7.0,accepted
3.0,2.0,4.0,19.0,7.0,0.0,3.0,1.0,inf,-17.0,11.0,accepted
4.0,2.0,3.0,17.0,8.0,0.0,3.0,1.0,inf,-16.0,10.0,accepted
3.0,4.0,2.0,17.0,11.0,0.0,3.0,1.0,0.0,-16.0,10.0,accepted
2.0,4.0,3.0,19.0,10.0,0.0,3.0,1.0,0.0,-17.0,11.0,accepted
4.0,2.0,3.0,17.0,8.0,0.0,3.0,1.0,inf,-17.0,11.0,accepted
2.0,3.0,4.0,20.0,8.0,0.0,3.0,1.0,inf,-16.0,10.0,accepted
2.0,4.0,3.0,19.0,10.0,0.0,3.0,2.0,inf,-17.0,8.0,accepted
2.0,3.0,4.0,20.0,8.0,0.0,3.0,2.0,inf,-19.0,10.0,accepted
3.0,4.0,2.0,17.0,11.0,0.0,3.0,1.0,inf,-16.0,10.0,accepted
2.0,4.0,3.0,19.0,10.0,0.0,3.0,1.0,inf,-16.0,10.0,accepted
4.0,2.0,3.0,17.0,8.0,0.0,3.0,1.0,inf,-16.0,10.0,accepted
2.0,3.0,4.0,20.0,8.0,0.0,3.0,1.0,inf,-17.0,11.0,accepted
2.0,4.0,3.0,19.0,10.0,0.0,3.0,2.0,inf,-17.0,8.0,accepted
2.0,3.0,4.0,20.0,8.0,0.0,3.0,2.0,inf,-19.0,10.0,accepted
3.0,2.0,4.0,19.0,7.0,3.0,4.0,1.0,inf,-17.0,11.0,accepted
4.0,2.0,3.0,17.0,8.0,3.0,4.0,1.0,inf,-16.0,10.0,accepted
2.0,3.0,4.0,20.0,8.0,3.0,4.0,1.0,0.0,-16.0,10.0,accepted
4.0,2.0,3.0,17.0,8.0,3.0,4.0,1.0,0.0,-17.0,11.0,accepted
3.0,2.0,4.0,19.0,7.0,3.0,4.0,1.0,0.0,-16.0,10.0,accepted
4.0,2.0,3.0,17.0,8.0,3.0,4.0,1.0,0.0,-17.0,11.0,accepted
2.0,4.0,3.0,19.0,10.0,3.0,4.0,1.0,0.0,-17.0,11.0,accepted
3.0,4.0,2.0,17.0,11.0,3.0,4.0,1.0,inf,-16.0,10.0,accepted
4.0,3.0,2.0,16.0,10.0,3.0,4.0,1.0,inf,-17.0,11.0,accepted
3.0,4.0,2.0,17.0,11.0,3.0,4.0,2.0,inf,-17.0,8.0,accepted
3.0,4.0,2.0,17.0,11.0,3.0,5.0,1.0,inf,-17.0,11.0,accepted
3.0,4.0,2.0,17.0,11.0,3.0,5.0,1.0,inf,-16.0,10.0,accepted
4.0,3.0,2.0,16.0,10.0,3.0,5.0,1.0,0.0,-16.0,10.0,accepted
3.0,4.0,2.0,17.0,11.0,3.0,5.0,1.0,0.0,-17.0,11.0,accepted
4.0,2.0,3.0,17.0,8.0,3.0,5.0,1.0,0.0,-16.0,10.0,accepted
3.0,2.0,4.0,19.0,7.0,3.0,5.0,1.0,0.0,-17.0,11.0,accepted
4.0,3.0,2.0,16.0,10.0,3.0,5.0,1.0,0.0,-16.0,10.0,accepted
3.0,4.0,2.0,17.0,11.0,3.0,5.0,1.0,0.0,-17.0,11.0,accepted
4.0,2.0,3.0,17.0,8.0,3.0,5.0,1.0,inf,-17.0,11.0,accepted
3.0,4.0,2.0,17.0,11.0,3.0,5.0,1.0,inf,-16.0,10.0,accepted
3.0,4.0,2.0,17.0,11.0,3.0,5.0,1.0,inf,-17.0,11.0,final
4.0,3.0,2.0,16.0,10.0,3.0,5.0,1.0,inf,-16.0,10.0,final
2.0,4.0,3.0,19.0,10.0,0.0,3.0,2.0,inf,-19.0,10.0,accepted
2.0,3.0,4.0,20.0,8.0,0.0,3.0,2.0,inf,-17.0,8.0,accepted
3.0,4.0,2.0,17.0,11.0,0.0,4.0,1.0,inf,-17.0,11.0,accepted
4.0,3.0,2.0,16.0,10.0,0.0,4.0,1.0,inf,-17.0,11.0,accepted
3.0,2.0,4.0,19.0,7.0,0.0,4.0,1.0,inf,-16.0,10.0,accepted
4.0,2.0,3.0,17.0,8.0,0.0,4.0,1.0,inf,-16.0,10.0,accepted
3.0,4.0,2.0,17.0,11.0,0.0,4.0,1.0,inf,-16.0,10.0,accepted
4.0,3.0,2.0,16.0,10.0,0.0,4.0,1.0,inf,-17.0,11.0,accepted
3.0,2.0,4.0,19.0,7.0,0.0,4.0,1.0,inf,-17.0,11.0,accepted
4.0,3.0,2.0,16.0,10.0,0.0,4.0,1.0,inf,-16.0,10.0,accepted
3.0,4.0,2.0,17.0,11.0,0.0,4.0,1.0,inf,-17.0,11.0,accepted
4.0,3.0,2.0,16.0,10.0,0.0,4.0,1.0,inf,-16.0,10.0,accepted
4.0,2.0,3.0,17.0,8.0,1.0,5.0,1.0,inf,-17.0,11.0,accepted
3.0,4.0,2.0,17.0,11.0,1.0,5.0,1.0,inf,-17.0,11.0,accepted
3.0,4.0,2.0,17.0,11.0,1.0,5.0,1.0,inf,-16.0,10.0,accepted
3.0,4.0,2.0,17.0,11.0,1.0,5.0,1.0,inf,-16.0,10.0,accepted
3.0,4.0,2.0,17.0,11.0,1.0,5.0,1.0,inf,-16.0,10.0,accepted
3.0,4.0,2.0,17.0,11.0,1.0,5.0,1.0,inf,-17.0,11.0,accepted
3.0,4.0,2.0,17.0,11.0,1.0,5.0,1.0,inf,-17.0,11.0,accepted
4.0,3.0,2.0,16.0,10.0,1.0,5.0,1.0,inf,-16.0,10.0,accepted
3.0,4.0,2.0,17.0,11.0,1.0,5.0,1.0,inf,-17.0,11.0,accepted
4.0,2.0,3.0,17.0,8.0,1.0,5.0,1.0,inf,-16.0,10.0,accepted
3.0,4.0,2.0,17.0,11.0,1.0,5.0,1.0,inf,-17.0,11.0,final
4.0,3.0,2.0,16.0,10.0,1.0,5.0,1.0,inf,-16.0,10.0,final

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