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Author: mcychan

algorithm/APNsgaIII.py

@@ -58,14 +58,14 @@ def dualCtrlStrategy(self, population, bestNotEnhance, nMax):
             tumor.mutation(self._mutationSize, self._mutationProbability)
 
             self._worst = population[-1]
-            if self.dominate(tumor, chromosome):
+            if tumor.dominates(chromosome):
                 population[i] = tumor
-                if self.dominate(tumor, self._best):
+                if tumor.dominates(self._best):
                     self._best = tumor
             else:
                 if bestNotEnhance >= 15 and N < nMax:
                     N += 1
-                    if self.dominate(self._worst, tumor):
+                    if self._worst.dominates(tumor):
                         population.append(tumor)
                         self._worst = tumor
                     else:
@@ -124,7 +124,7 @@ def run(self, maxRepeat=9999, minFitness=0.999):
 
             # replacement
             pop[next] = self.replacement(pop[cur])
-            self._best = pop[next][0] if self.dominate(pop[next][0], pop[cur][0]) else pop[cur][0]
+            self._best = pop[next][0] if pop[next][0].dominates(pop[cur][0]) else pop[cur][0]
 
             self.dualCtrlStrategy(pop[next], bestNotEnhance, nMax)
 

algorithm/NsgaIII.py

@@ -257,16 +257,6 @@ def normalizeObjectives(self, pop, fronts, intercepts, idealPoint):
                     else:
                         convObj /= np.finfo(float).eps
 
-    def dominate(self, left, right):
-        better = False
-        for f, leftObj in enumerate(left.objectives):
-            if leftObj > right.objectives[f]:
-                return False
-
-            if leftObj < right.objectives[f]:
-                better = True
-
-        return better
 
     def nondominatedSort(self, pop):
         numAssignedIndividuals, rank = 0, 1
@@ -281,10 +271,10 @@ def nondominatedSort(self, pop):
 
                 be_dominated = False
                 for j, front in enumerate(curFront):
-                    if self.dominate(pop[front], chromosome):
+                    if pop[front].dominates(chromosome):
                         be_dominated = True
                         break
-                    elif self.dominate(chromosome, pop[front]):
+                    elif chromosome.dominates(pop[front]):
                         del curFront[j]
 
                 if not be_dominated:
@@ -462,7 +452,7 @@ def run(self, maxRepeat=9999, minFitness=0.999):
 
             # replacement
             pop[next] = self.replacement(pop[cur])
-            self._best = pop[next][0] if self.dominate(pop[next][0], pop[cur][0]) else pop[cur][0]
+            self._best = pop[next][0] if pop[next][0].dominates(pop[cur][0]) else pop[cur][0]
 
             cur, next = next, cur
             currentGeneration += 1

model/Schedule.py

@@ -420,3 +420,14 @@ def resizeConvertedObjectives(self, numObj):
 
     def clone(self):
         return self.copy(self, False)
+
+    def dominates(self, other):
+        better = False
+        for f, obj in enumerate(self.objectives):
+            if obj > other.objectives[f]:
+                return False
+
+            if obj < other.objectives[f]:
+                better = True
+
+        return better