y = f(x)
## Optimisation
import sys
sys.path.append("..")
from _simulated_annealing import minimize_simulatedAnnealing
cons = [{'type': 'strictIneq', 'fun': c1}]
maxIter = 300
Xsa = []
for i in range(100):
xi = minimize_simulatedAnnealing(f, [2.7], [7.5],
maxIter=maxIter,
constraints=cons,
autoSetUpIter=100)
Xsa.append(xi)
Xsa = np.array(Xsa)
Ysa = f(Xsa)
## Graphe
figure1 = plt.figure(1, figsize=(8, 3))
plt.plot(x, y, color='k', label="y = f(x)")
plt.plot(Xsa,
Ysa,
label="Solution opitmisation",
marker='o',
ls='',
cons = [{'type': 'ineq', 'fun': sim.contrainte1},
{'type': 'ineq', 'fun': sim.contrainte2},
{'type': 'ineq', 'fun': sim.contrainte3},
{'type': 'ineq', 'fun': sim.contrainte4},
{'type': 'ineq', 'fun': sim.contrainte5},
{'type': 'ineq', 'fun': sim.contrainte6}]
maxIter = 10000
mindict = minimize_simulatedAnnealing(sim.cost,
xmin,
xmax,
maxIter=maxIter,
constraints=cons,
preprocess_function=sim.simulateHeatPump,
autoSetUpIter=100,
config="lowTemp",
verbose=False,
returnDict=True,
storeIterValues=True)
Xsa = mindict['x']
fhistory = mindict["fHistory"]
sim.printDictSim(Xsa)
print(Xsa)
print(sim.cost(Xsa))
plt.figure(figsize=(8,4))
sys.path.append("..")
from _simulated_annealing import minimize_simulatedAnnealing
cons = [{'type': 'eq', 'fun': c0}]
autoSetUpIter = 250
maxIter = 10000
listXsa = []
xopt, yopt = None, None
for i in range(10):
print("#SOLVE : ", i)
mindict = minimize_simulatedAnnealing(f0,
xmin,
xmax,
maxIter=maxIter - autoSetUpIter,
autoSetUpIter=autoSetUpIter,
constraintAbsTol=0.001,
penalityFactor=0.1,
returnDict=True,
config='highTemp',
constraints=cons)
Xsa = mindict["x"]
Ysa = mindict["f"]
if yopt is None:
xopt = Xsa
yopt = Ysa
elif yopt > Ysa:
xopt = Xsa
yopt = Ysa
listXsa.append(Xsa)
npop = 36
ngen = (maxIter+1)//npop
ga_instance = realSingleObjectiveGA(f0,xmin,xmax,constraints=cons,tol=-1.0,constraintMethod="penality")
for k in range(nloop):
print("LOOP : ",k)
print("")
#genetic algorithm
ga_instance.minimize(npop,ngen,verbose=False)
fitness_ga = ga_instance.getStatOptimisation()
ga_convergence.append(fitness_ga)
#simulated annealing
mindict_sa = minimize_simulatedAnnealing(f0,xmin,xmax,maxIter=maxIter,constraints=cons,returnDict=True,storeIterValues=True)
sa_convergence.append(mindict_sa["fHistory"])
#differential evolution
mindict_de = differential_evolution(f0,xmin,xmax,maxIter=ngen,popsize=npop,constraints=cons,returnDict=True,storeIterValues=True,tol=-1)
de_convergence.append(mindict_de["fHistory"])
# Optimisation locale
bounds = [(xi,xj) for xi,xj in zip(xmin,xmax)]
startX = np.mean(bounds,axis=1)
res = minimize(f0,mindict_sa["x"],bounds=bounds)
fscipy = res.fun
ga_convergence = np.log10((np.array(ga_convergence).T - fscipy)/abs(fscipy))
## Optimisation
import sys
sys.path.append("..")
from _simulated_annealing import simulatedAnnealing, minimize_simulatedAnnealing
from _minimize_NelderMead import *
from _minimize_Powell import *
cons = []
listXsa = []
maxIter = 500
for i in range(10):
mindict = minimize_simulatedAnnealing(f0,
xmin,
xmax,
maxIter=maxIter,
autoSetUpIter=100,
returnDict=True,
storeIterValues=True)
Xsa = mindict["x"]
fitnessArray = mindict["fHistory"]
listXsa.append(Xsa)
listXsa = np.array(listXsa)
# for si in mindict :
# if not( si.endswith("History") ):
# print(si," : ",mindict[si])
## SCIPY
# bounds = [(xi,xj) for xi,xj in zip(xmin,xmax)]