def onestep(nInput, nOutput, xlb, xub, pct, \
Xinit, Yinit, pop = 100, gen = 100, \
crossover_rate = 0.9, mu = 20, mum = 20):
"""
Multi-Objective Adaptive Surrogate Modelling-based Optimization
One-step mode for offline optimization
Do NOT call the model evaluation function
nInput: number of model input
nOutput: number of output objectives
xlb: lower bound of input
xub: upper bound of input
pct: percentage of resampled points in each iteration
Xinit and Yinit: initial samplers for surrogate model construction
### options for the embedded NSGA-II of MO-ASMO
pop: number of population
gen: number of generation
crossover_rate: ratio of crossover in each generation
mu: distribution index for crossover
mum: distribution index for mutation
"""
N_resample = int(pop * pct)
x = Xinit.copy()
y = Yinit.copy()
sm = gp.GPR_Matern(x, y, nInput, nOutput, x.shape[0], xlb, xub)
bestx_sm, besty_sm, x_sm, y_sm = \
NSGA2.optimization(sm, nInput, nOutput, xlb, xub, \
pop, gen, crossover_rate, mu, mum)
D = NSGA2.crowding_distance(besty_sm)
idxr = D.argsort()[::-1][:N_resample]
x_resample = bestx_sm[idxr, :]
return x_resample
def optimization(model, nInput, nOutput, xlb, xub, niter, pct, \
Xinit = None, Yinit = None, pop = 100, gen = 100, \
crossover_rate = 0.9, mu = 20, mum = 20):
"""
Multi-Objective Adaptive Surrogate Modelling-based Optimization
model: the evaluated model function
nInput: number of model input
nOutput: number of output objectives
xlb: lower bound of input
xub: upper bound of input
niter: number of iteration
pct: percentage of resampled points in each iteration
Xinit and Yinit: initial samplers for surrogate model construction
### options for the embedded NSGA-II of MO-ASMO
pop: number of population
gen: number of generation
crossover_rate: ratio of crossover in each generation
mu: distribution index for crossover
mum: distribution index for mutation
"""
N_resample = int(pop * pct)
if (Xinit is None and Yinit is None):
Ninit = nInput * 10
Xinit = sampling.glp(Ninit, nInput)
for i in range(Ninit):
Xinit[i, :] = Xinit[i, :] * (xub - xlb) + xlb
Yinit = np.zeros((Ninit, nOutput))
for i in range(Ninit):
Yinit[i, :] = model.evaluate(Xinit[i, :])
else:
Ninit = Xinit.shape[0]
icall = Ninit
x = Xinit.copy()
y = Yinit.copy()
for i in range(niter):
print('Surrogate Opt loop: %d' % i)
sm = gp.GPR_Matern(x, y, nInput, nOutput, x.shape[0], xlb, xub)
bestx_sm, besty_sm, x_sm, y_sm = \
NSGA2.optimization(sm, nInput, nOutput, xlb, xub, \
pop, gen, crossover_rate, mu, mum)
D = NSGA2.crowding_distance(besty_sm)
idxr = D.argsort()[::-1][:N_resample]
x_resample = bestx_sm[idxr, :]
y_resample = np.zeros((N_resample, nOutput))
for j in range(N_resample):
y_resample[j, :] = model.evaluate(x_resample[j, :])
icall += N_resample
x = np.vstack((x, x_resample))
y = np.vstack((y, y_resample))
xtmp = x.copy()
ytmp = y.copy()
xtmp, ytmp, rank, crowd = NSGA2.sortMO(xtmp, ytmp, nInput, nOutput)
idxp = (rank == 0)
bestx = xtmp[idxp, :]
besty = ytmp[idxp, :]
return bestx, besty, x, y
def main():
nIter = 50
nChr = 3
nPop = 100
pc = 0.6
pm = 0.1
etaC = 1
etaM = 1
func = function
lb = -2
rb = 2
paretoPops, paretoFits = NSGA2(nIter, nChr, nPop, pc, pm, etaC, etaM, func,
lb, rb)
print(paretoFits)
print(f"paretoFront: {paretoFits.shape}")
# 理论最优解集合
x = np.linspace(-1 / np.sqrt(3), 1 / np.sqrt(3), 116).reshape(116, 1)
X = np.concatenate((x, x, x), axis=1)
thFits = fitness(X, function)
plt.rcParams['font.sans-serif'] = 'KaiTi' # 设置显示中文
fig = plt.figure(dpi=400)
ax = fig.add_subplot(111)
ax.plot(thFits[:, 0], thFits[:, 1], color='green', label='理论帕累托前沿')
ax.scatter(paretoFits[:, 0], paretoFits[:, 1], color='red', label='实际解集')
ax.legend()
fig.savefig('test.png', dpi=400)
print(paretoPops)
def run_MOEA(MOEA, SIR_name, version):
result = []
num_testcases = GLOB.NUM_TESTCASES[SIR_name]
for i in range(GLOB.TRIALS_PER_VERSION):
print(SIR_name, 'version', version, '_', MOEA, ': trial', i)
if MOEA == 'NSGA2':
res = NSGA2.run_NSGA2(SIR_name, version, num_testcases)
result.append(res)
elif MOEA == 'SPEA2':
res = SPEA2.run_SPEA2(SIR_name, version, num_testcases)
result.append(res)
elif MOEA == 'TAEA':
res = TAEA.run_TAEA(SIR_name, version, num_testcases)
result.append(res)
write_file(MOEA, SIR_name, version, result)
import NSGA2
import pandas as pd
test = pd.read_excel("TestData.xlsx")
test_pkl = pd.read_pickle("bugzilla_eclipse_log(comments)_2016meancost.pkl")
print(test_pkl['Profit'].shape)
print(test_pkl['Cost'].shape)
test_SA = SIM_ANEAL.NRP_SA(profit=test_pkl['Profit'],
cost=test_pkl['Cost'],
bound=50699.701903571,
iteration=100000,
init_decision=np.random.randint(
0, 2, test_pkl['Profit'].shape))
test_GA = GA.NRP_GA(profit=test_pkl['Profit'],
cost=test_pkl['Cost'],
bound=50699.701903571,
iteration=5000)
test_NSGA2 = NSGA2.NRP_NSGA2(profit=test_pkl['Profit'],
cost=test_pkl['Cost'],
bound=50699.701903571,
iteration=5000)
# result_SA = test_SA.run_SA()
result_GA = test_GA.run_GA()
# result_NSGA2 = test_NSGA2.run_NSGA2()
print(result_SA)
# print(result_NSGA2)
networkTopologyStr = networkTopology
cnf = config.CONFIG()
cnf.topologyGraphml = networkTopology
cnf.numberOfReplicatedApps = numApps
system = systemmodel.SYSTEMMODEL(cnf)
#no permite bucles
#los servicios y fog devices deben de estar ordenador desde 0 y con ids consecutivos, sin dejar ninguno en blanco
system.FGCSmodel3(cnf.modelSeed)
if gatype == 'weightedga':
#g = ga.GA(system,300,28)
g = wga.weightedGA(system, cnf.populationSeed, cnf.evolutionSeed, cnf)
if gatype == 'nsga2':
g = nsga2.NSGA2(system, cnf.populationSeed, cnf.evolutionSeed, cnf)
if gatype == 'moead':
g = moead.MOEAD(system, cnf.populationSeed, cnf.evolutionSeed, cnf)
generationSolution = list()
generationPareto = list()
minV = float('inf')
minIdx = -1
for idx, v in enumerate(g.corega.populationPt.fitness):
if v['total'] < minV:
minIdx = idx
minV = v['total']
if len(g.corega.populationPt.population) > 0:
print g.corega.populationPt.fitness[minIdx]
file=routes)
vehNr += 1
print("</routes>", file=routes)
# this is the main entry point of this script
if __name__ == "__main__":
import time
array = np.array([49, 5, 9, 17, 4, 34, 1, 56, 3, 87])
t = time.time()
function_Cross_3_3(array)
print("time for one function evaluation: ")
print(time.time() - t)
print(50 * "=")
# print("optimisation")
# opt1, fD1 = HC.hillClimbing(array, function_Cross_3_3, stepSize=1, functionEvaluation=1)
# print("passed HC")
# opt2, fD2 = CGD.ConjugateGradientDescent(array, function_Cross_3_3, epsilon=10, alpha=0.1, eta=10, h=np.finfo(np.float64).eps)
# print("passed CGD")
# pop = np.random.rand(5,4)*np.random.randint(-100, 100)
# opt3, fD3 = DE.DifferentialEvolution(pop, function_Cross_3_3, maxFunctionEval=6, F=0.5, CR=0.1)
# print("passed DE")
pop = NSGA2.populationInitialisationNSGA2(
function_Cross_3_3, 5, [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],
[70, 70, 70, 70, 70, 70, 70, 70, 70, 70])
print("finished init")
opt4, fD4 = NSGA2.NSGA2(pop, function_Cross_3_3, maxGeneration=2)
# print(time.time() - t)
# print("passed NSGA2")
# print(50*"=")
# print("passed test")
sys.path.append('../../Modules')
import NSGA2
import Cross_3_3_night as c33
if __name__ == "__main__":
# calculating
# print("start init random")
# pop = NSGA2.populationInitialisationNSGA2(c33.function_Cross_3_3, 30, [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [70, 70, 70, 70, 70, 70, 70, 70, 70, 70])
print("read old pop")
with open('NSGA2_SaveList.pkl', 'rb') as pickle_file:
backup = pickle.load(pickle_file)
pop = backup[-1][0]
print("finished init")
opt4, fD4 = NSGA2.NSGA2(pop, c33.function_Cross_3_3, maxGeneration=6)
# plotting 3D Scatter
# from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import
# import matplotlib.pyplot as plt
# with open('NSGA2_SaveList.pkl', 'rb') as pickle_file:
# backup = pickle.load(pickle_file)
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# plotPoints = backup[-1][1][-30:]
# for x, y, z in plotPoints:
# print(str(x) + " " + str(y) + " " + str(z))
# ax.scatter(x, y, z)
# ax.set_xlabel('X Label')
# ax.set_ylabel('Y Label')
# load parameter name and range
pf = util.read_param_file('%s.txt' % modelname)
bd = np.array(pf['bounds'])
nInput = pf['num_vars']
nOutput = 2
xlb = bd[:, 0]
xub = bd[:, 1]
# parameters for NSGA2
pop = 100
gen = 100
# run NSGA2
bestx, besty, x, y = \
NSGA2.optimization(model, nInput, nOutput, xlb, xub, pop, gen)
# plot results
plt.plot(y[:, 0], y[:, 1], 'b.', label='evaluated points')
plt.plot(besty[:, 0], besty[:, 1], 'r.', label='NSGA2 optimal')
model_true = __import__(modelname + '_true')
y_true = model_true.pareto()
plt.plot(y_true[:, 0], y_true[:, 1], 'k-', label='True Pareto')
plt.xlabel('y1')
plt.ylabel('y2')
plt.legend()
# save figure
plt.savefig('%s/ZDT1_NSGA2.png' % respath)