def run1(self):
timeStep = 1
while timeStep <= self.timeSteps:
self.paramsGA1["simulator"].clear()
self.paramsGA1["timeSteps"] = timeStep
ga1 = GA1(self.paramsGA1)
bestIndividuals = ga1.run()
newPopulation = self.addTimeStep(bestIndividuals)
self.paramsGA1["populationGA1"] = newPopulation
if timeStep <= self.timeSteps//2:
timeStep *= 2
elif timeStep < self.timeSteps and timeStep > self.timeSteps//2:
timeStep = self.timeSteps
elif timeStep == self.timeSteps:
break
bestIndividual = newPopulation[0][0:self.params["crossroads"]]
self.params["simulator"].clear()
self.params["simulator"].setState(bestIndividual)
fitness = 0
for timeStep in range(self.params["timeSteps"]):
population = []
for individual in newPopulation:
population.append(
individual[timeStep*self.params["crossroads"]:(timeStep+1)*self.params["crossroads"]])
print("Timtestep: " + str(timeStep))
self.paramsGA2["densities"] = None
self.paramsGA2["population"] = population
ga2 = GA2(self.paramsGA2)
best, bestIndividual = ga2.run()
fitness += best
self.params["simulator"].setState(bestIndividual)
print(fitness)
self.params["simulator"].exit()
return fitness
def run1(self):
newPopulation = self.params["populationGA2"]
if newPopulation is not None:
bestIndividual = newPopulation[0][0:self.params["crossroads"]]
fitness = 0
for timeStep in range(self.params["timeSteps"]):
if newPopulation is not None:
population = []
for individual in newPopulation:
population.append(individual[timeStep*self.params["crossroads"]:(timeStep+1)*self.params["crossroads"]])
else:
population = None
print("Timtestep: " + str(timeStep))
self.paramsGA2["population"] = population
ga2 = GA2(self.paramsGA2)
best, bestIndividual = ga2.run()
fitness+=best
self.positions[timeStep] = self.params["simulator"].getPositions(bestIndividual[0], True)
self.params["simulator"].setState(bestIndividual)
print(self.positions)
print(fitness)
self.params["simulator"].exit()
return fitness
def run2(self):
fitness = 0
for timeStep in range(self.params["timeSteps"]):
self.paramsGA2["densities"] = None
self.paramsGA2["population"] = None
ga2 = GA2(self.paramsGA2)
fitness += ga2.run()
self.params["simulator"].exit()
return fitness
def run2(self):
self.params["simulator"].clear()
fitness = 0
for timeStep in range(self.params["timeSteps"]):
print("Timtestep: " + str(timeStep))
self.paramsGA2["densities"] = None
self.paramsGA2["population"] = None
ga2 = GA2(self.paramsGA2)
best, improvement, population = ga2.run()
fitness += best
self.params["simulator"].setState(population)
print(fitness)
self.params["simulator"].clear()
return 0
def run2(self):
self.ga1.run()
self.params["simulator"].changeRoutes()
print("\n\n\nGA2:")
print("*"*40)
for timeStep in range(self.timeSteps):
print("*"*40)
print("Time step: " + str(timeStep+1))
self.paramsGA2["densities"] = self.ga1.getDensities(timeStep)
self.paramsGA2["population"] = self.ga1.getTimings(timeStep)
ga2 = GA2(self.paramsGA2)
ga2.run()
print("*"*40)
self.params["simulator"].exit()
def run(self, timeStep):
newPopulation = self.params["populationGA2"]
if newPopulation is None:
population = None
elif any(isinstance(i, list) for i in newPopulation):
population = []
for individual in newPopulation:
population.append(individual[timeStep*self.params["crossroads"]:(timeStep+1)*self.params["crossroads"]])
else:
population = []
population.append(newPopulation[timeStep*self.params["crossroads"]:(timeStep+1)*self.params["crossroads"]])
self.paramsGA2["population"] = population
ga2 = GA2(self.paramsGA2)
fitness, improvement, bestIndividual = ga2.run()
positions = self.params["simulator"].getPositions(bestIndividual)
self.params["simulator"].setState(bestIndividual)
return improvement, bestIndividual[0], positions
def run2(self):
timings = []
self.params["simulator"].clear()
fitness = 0
for timeStep in range(self.params["timeSteps"]):
print("Timtestep: " + str(timeStep))
self.paramsGA2["densities"] = None
self.paramsGA2["population"] = None
ga2 = GA2(self.paramsGA2)
best, improvement, population = ga2.run()
timings.append(population[0])
fitness+=best
self.params["simulator"].setState(population)
print(fitness)
print(timings)
print(fitness)
print(timings)
self.params["simulator"].exit()
return ((worstFitness-bestFitness)/worstFitness)*100