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SimulatedAnnealing.py
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SimulatedAnnealing.py
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import AnnealingFunctions
import random
class SimulatedAnnealing(object):
def __init__(self,problem_size,max_iterations,temp_max,temp_change,KnapsackObj):
self.ProblemSize = problem_size
self.MaxIterations = max_iterations
self.MaxTemp = temp_max
self.temp_change = temp_change
self.AF = AnnealingFunctions.AnnealingFunctions(KnapsackObj)
def run_sa(self):
current = self.AF.getRandomSolution()
best = current
temp = self.MaxTemp
x = [0]
y = [self.AF.getValue(best)]
for i in range(self.MaxIterations):
neigbourSolution = self.AF.getNeighbouringSolution(current)
temp = self.AF.getTemperature(i,temp,self.temp_change)
siCost = self.AF.getValue(neigbourSolution)
sCost = self.AF.getValue(current)
if siCost <= sCost:
current = neigbourSolution
if siCost <= self.AF.getValue(best):
best = neigbourSolution
elif self.AF.getMonteCarlo(sCost,siCost,temp) > random.uniform(0,1):
current = neigbourSolution
print("hello")
x.append(i+1)
y.append(-1*self.AF.getValue(current))
return best,x,y