def moea(name, solsize, popsize, wscalar_, moea_type, max_gen=float('inf'), timeLimit=float('inf')):
from platypus import HUX, BitFlip, TournamentSelector
from platypus import Problem, Binary
from platypus import NSGAII, NSGAIII, SPEA2
from platyplus.operators import varOr
from platyplus.algorithms import SMSEMOA
time_start = time.perf_counter()
logger.info('Running '+moea_type+' in '+name)
prMutation = 0.1
prVariation = 1-prMutation
vartor = varOr(HUX(), BitFlip(1), prVariation, prMutation)
def evalKnapsack(x):
return wscalar_.fobj([xi[0] for xi in x])
problem = Problem(wscalar_.N, wscalar_.M)
problem.types[:] = [Binary(1) for i in range(wscalar_.N)]
problem.function = evalKnapsack
if moea_type in ['NSGAII', 'NSGAII-2', 'NSGAII-4']:
alg = NSGAII(problem, population_size=popsize,
selector=TournamentSelector(1),
variator=vartor)
elif moea_type in ['NSGAIII', 'NSGAIII-2', 'NSGAIII-4']:
alg = NSGAIII(problem, divisions_outer=3,
population_size=popsize,
selector=TournamentSelector(1),
variator=vartor)
elif moea_type in ['SPEA2', 'SPEA2-2', 'SPEA2-4']:
alg = SPEA2(problem, population_size=popsize,
selector=TournamentSelector(1),
variator=vartor)
elif moea_type in ['SMSdom']:
alg = SMSEMOA(problem, population_size=popsize,
selector=TournamentSelector(1),
variator=vartor,
selection_method = 'nbr_dom')
elif moea_type in ['SMShv']:
alg = SMSEMOA(problem, population_size=popsize,
selector=TournamentSelector(1),
variator=vartor,
selection_method = 'hv_contr')
gen = 1
while gen<max_gen and time.perf_counter()-time_start<timeLimit:
alg.step()
gen+=1
alg.population_size = solsize
alg.step()
moeaSols = [evalKnapsack(s.variables) for s in alg.result]
moea_time = time.perf_counter() - time_start
logger.info(moea_type+' in '+name+' finnished.')
return moeaSols, moea_time
from platypus import nondominated
from hypervolume import hypervolume
import ast
import random
random.seed(0)
for d in range(1, 10):
for g in range(1, 10):
hyp = []
nfes = []
for i in range(10):
problem = Problem(2, 2, 2)
problem.types[:] = [Real(0, 5), Real(0, 3)]
problem.constraints[:] = "<=0"
problem.function = BNH
algorithm = NSGAIII(problem, d * problem.nvars)
algorithm.run(d * g * problem.nvars)
funcname = 'BNH'
# if not os.path.exists(funcname):
# os.makedirs(funcname)
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([140, 50])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
# np.savetxt(str(funcname)+'/'+str(funcname)+'_pf_run_'+str(i)+'.csv', obj, delimiter=',')
hyp.append(hypervolume(obj, ref))
from platypus import NSGAIII, DTLZ2
# define the problem definition
problem = DTLZ2(3)
# instantiate the optimization algorithm
algorithm = NSGAIII(problem, divisions_outer=12)
# optimize the problem using 10,000 function evaluations
algorithm.run(1000)
# plot the results using matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter([s.objectives[0] for s in algorithm.result],
[s.objectives[1] for s in algorithm.result],
[s.objectives[2] for s in algorithm.result])
plt.show()
from platypus import NSGAIII, DTLZ2, Recorder
from platypus import MainWindow
from PyQt5.QtGui import QApplication
# define the problem definition
problem = DTLZ2(3)
# instantiate the optimization algorithm
algorithm = NSGAIII(problem, divisions_outer=12)
# optimize the problem using 10,000 function evaluations
recorder = Recorder(save_result=True, save_all=False)
# GUI version
app = QApplication([])
window = MainWindow(nobjs=problem.nobjs, algorithm=algorithm, nfe=10000, recorder=recorder)
app.exec_()
# # non-GUI version
# algorithm.run(10000, recorder=recorder)
# plot the results using matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter([s.objectives[0] for s in algorithm.result],
[s.objectives[1] for s in algorithm.result],
[s.objectives[2] for s in algorithm.result])
plt.show()
hyp = []
for i in range(5):
ref = np.array([5000, 2])
problem = Problem(6, 2, 16)
problem.types[:] = [
Real(5.0, 9.0),
Real(16.0, 22.0),
Real(5.0, 9.0),
Real(12.0, 16.0),
Real(-2.8, 9.8),
Real(-1.6, 3.4)
]
problem.constraints[:] = "<=0"
problem.function = problemCall
algorithm = NSGAIII(problem, len(ref))
algorithm.run(200)
nondominated_solutions = nondominated(algorithm.result)
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
print('ship', np.mean(hyp))
print('ship', np.max(hyp))
print('ship', np.std(hyp))
hyp = []
for i in range(100):
ref = np.array([7000, 1700])
fs = np.array(fs)
feasible = np.sum(cs <= 0, axis=1) == 3
for i in range(1, len(fs)):
fi = fs[:i][feasible[:i]]
nsgaiihv.append(hypervolume(fi, ref))
nsgaiihv = nsgaiihv[:30]
print(nsgaiihv[-1])
fs = []
cs = []
random.seed(0)
problem = Problem(2, 3, 3)
problem.types[:] = [Real(-4, 4), Real(-4, 4)]
problem.constraints[:] = "<=0"
problem.function = TRICOP
algorithm = NSGAIII(problem, 1)
algorithm.run(30)
nsgaiiihv = []
cs = np.array(cs)
fs = np.array(fs)
feasible = np.sum(cs <= 0, axis=1) == 3
for i in range(1, len(fs)):
fi = fs[:i][feasible[:i]]
nsgaiiihv.append(hypervolume(fi, ref))
nsgaiiihv = nsgaiiihv[:30]
print(nsgaiiihv[-1])
class TRICOP_c(Problem):
def __init__(self):
super().__init__(n_var=2, n_obj=3, n_constr=3)
def _pick_algorithm(myproblem,
algorithm,
population,
mutation_probability,
current_solution,
evaluator=None):
"""
Return a serial or parallel platypus.Algorithm object based on input string
:param myproblem: EZFF Problem to be optimized
:type myproblem: Problem
:param algorithm: EZFF Algorithm to use for optimization. Allowed options are ``NSGAII``, ``NSGAIII`` and ``IBEA``
:type algorithm: str
:param population: Population size for genetic algorithms
:type population: int
:param evaluator: Platypus.Evaluator in case of parallel execution
:type evaluator: Platypus.Evaluator
"""
if mutation_probability is None:
variator = GAOperator(SBX(), PM())
else:
variator = GAOperator(SBX(), PM(probability=mutation_probability))
print('Using provided mutation probability')
if algorithm.lower().upper() == 'NSGAII':
if evaluator is None:
if current_solution is None:
return NSGAII(myproblem,
population_size=population,
variator=variator)
else:
return NSGAII(myproblem,
population_size=population,
variator=variator,
generator=InjectedPopulation(
current_solution[0:population]))
else:
if current_solution is None:
return NSGAII(myproblem,
population_size=population,
variator=variator,
evaluator=evaluator)
else:
return NSGAII(myproblem,
population_size=population,
variator=variator,
generator=InjectedPopulation(
current_solution[0:population]),
evaluator=evaluator)
elif algorithm.lower().upper() == 'NSGAIII':
num_errors = len(myproblem.directions)
divisions = int(
np.power(population * np.math.factorial(num_errors - 1), 1.0 /
(num_errors - 1))) + 2 - num_errors
divisions = np.maximum(1, divisions)
if evaluator is None:
if current_solution is None:
return NSGAIII(myproblem,
divisions_outer=divisions,
variator=variator)
else:
return NSGAIII(myproblem,
divisions_outer=divisions,
variator=variator,
generator=InjectedPopulation(
current_solution[0:population]))
else:
if current_solution is None:
return NSGAIII(myproblem,
divisions_outer=divisions,
variator=variator,
evaluator=evaluator)
else:
return NSGAIII(myproblem,
divisions_outer=divisions,
variator=variator,
generator=InjectedPopulation(
current_solution[0:population]),
evaluator=evaluator)
elif algorithm.lower().upper() == 'IBEA':
if evaluator is None:
if current_solution is None:
return IBEA(myproblem,
population_size=population,
variator=variator)
else:
return IBEA(myproblem,
population_size=population,
variator=variator,
generator=InjectedPopulation(
current_solution[0:population]))
else:
if current_solution is None:
return IBEA(myproblem,
population_size=population,
variator=variator,
evaluator=evaluator)
else:
return IBEA(myproblem,
population_size=population,
variator=variator,
generator=InjectedPopulation(
current_solution[0:population]),
evaluator=evaluator)
else:
raise Exception(
'Please enter an algorithm for optimization. NSGAII , NSGAIII , IBEA are supported'
)
problem.function = uswitch_objective
# Run optimization in parallel
n_proc = 4
n_evals = 100000
alg_name = 'NSGAII'
#alg_name = 'NSGAIII'
#alg_name = 'SPEA2'
start_time = time.time()
with ProcessPoolEvaluator(n_proc) as evaluator:
if alg_name=='NSGAII':
algorithm = NSGAII(problem,evaluator=evaluator)
elif alg_name=='NSGAIII':
divs = 20
alg_name=alg_name+str(divs)
algorithm = NSGAIII(problem,divisions_outer=div,evaluator=evaluator)
elif alg_name=='SPEA2':
algorithm = SPEA2(problem,evaluator=evaluator)
else:
print("Using NSGAII algorithm by default")
algorithm = NSGAII(problem,evaluator=evaluator)
algorithm.run(n_evals)
print("Total run time = {:.2f} hours".format((time.time()-start_time)/3600.))
# Save Results
save_dir = "./Data/U_switch/plat/"
pareto_file_name = save_dir + "pareto_front_{0}_{1}_{2}_".format(d,tau_p,n_evals) + alg_name + "_dtmax{0}.npy".format(dtmax)
points = []
costs = []
for s in algorithm.result:
points.append(s.variables[:])
# print(solution.objectives[:])
def random(self):
solution = ps.Solution(self)
solution.variables[:] = [
random.uniform(0, 5) for _ in range(self.types)
]
solution.evaluate()
return solution
# define the problem definition
problem = MyProblem()
# instantiate the optimization algorithm
algorithm = NSGAIII(problem, 20)
# optimize the problem using 10,000 function evaluations
algorithm.run(10000)
# plot the results using matplotlib
import matplotlib.pyplot as plt
plt.scatter([s.objectives[0] for s in algorithm.result],
[s.objectives[1] for s in algorithm.result])
plt.xlim([-5.1, 5.1])
plt.ylim([-5.1, 5.1])
plt.xlabel("$f_1(x)$")
plt.ylabel("$f_2(x)$")
plt.show()
def my_graph(points, edges):
global n_dim
global fig
number = 0
d1 = pd.read_csv(points)
d2 = pd.read_csv(edges)
n_dim = len(d1.columns)
m1 = 'MDS'
m2 = 'MOPSO-NSGAII'
m3 = 'MOPSO-NSGAIII'
# ## MDS
mds = MDS(n_components=2)
dat = pd.DataFrame(mds.fit(d1).embedding_, columns=["x1", "x2"])
dij = manhattan_distances(d1.values, d1.values)
dis2 = manhattan_distances(dat.values, dat.values)
mds_stress = (np.sum((dij - dis2)**2))**0.5
mds_inter = number_of_intersection(dat["x1"], dat["x2"], d2)
graph(dat, d2, m1, number, 1, mds_stress, mds_inter)
print "MDS Stress : ", mds_stress
print "Total Intersection MDS = ", mds_inter
# ## Multi Objective Particle Swarm Optimization (MOPSO)
def objective_mopso(x):
twoD = np.reshape(x, (-1, 2))
dis = manhattan_distances(twoD, twoD)
stress = (np.sum((dij - dis)**2))**0.5
mopso_inter = number_of_intersection(x[0:][::2], x[1:][::2], d2)
return [stress, mopso_inter]
problem = Problem(len(d1) * 2, 2)
problem.types[:] = Real(-50, 50)
problem.function = objective_mopso
ngsaii = NSGAII(problem, population_size=100)
ngsaii.run(10000)
result = ngsaii.result
ngsaiii = NSGAIII(problem, population_size=100, divisions_outer=100)
ngsaiii.run(10000)
result2 = ngsaiii.result
ax = fig.add_subplot(1, 4, 4)
lngsaii = plt.scatter([s.objectives[0] for s in ngsaii.result],
[s.objectives[1] for s in ngsaii.result],
color='b')
lngsaiii = plt.scatter([s.objectives[0] for s in ngsaiii.result],
[s.objectives[1] for s in ngsaiii.result],
color='g')
lmds, = plt.plot(mds_stress, mds_inter, 'ro', markersize=8)
y_min, y_max = ax.get_ylim()
x_min, x_max = ax.get_xlim()
plt.xticks(
np.arange(math.floor(x_min), math.ceil(x_max),
math.ceil((math.ceil(x_max) - math.ceil(x_min)) / 5)))
if math.ceil(y_min) == math.floor(y_max):
plt.yticks(np.arange(math.ceil(y_min - 1.1), math.ceil(y_max + 1.1),
1))
else:
plt.yticks(
np.arange(
math.floor(y_min - .1), math.ceil(y_max + .1),
math.ceil(
(math.ceil(y_max + .1) - math.ceil(y_min - .1)) / 5)))
plt.savefig('./output/graph' + str(number) + '.png', bbox_inches='tight')
count = 0
out = {}
ngsaii_inter = result[0].objectives[1]
ngsaii_stress = result[0].objectives[0]
val = 0
for solution in ngsaii.result:
out[solution.objectives] = solution.variables
if solution.objectives[1] < ngsaii_inter:
ngsaii_inter = solution.objectives[1]
ngsaii_stress = solution.objectives[0]
val = count
count += 1
sample = out[result[val].objectives]
x = sample[0:][::2]
y = sample[1:][::2]
newD = pd.DataFrame({"x1": x, "x2": y})
print "Stress for NGSAII = ", ngsaii_stress
print "Total Intersection NGSAII = ", ngsaii_inter
graph(newD, d2, m2, number, 2, ngsaii_stress, ngsaii_inter)
count = 0
out = {}
ngsaiii_inter = result2[0].objectives[1]
ngsaiii_stress = result2[0].objectives[0]
val = 0
for solution in ngsaiii.result:
out[solution.objectives] = solution.variables
if solution.objectives[1] < ngsaiii_inter:
ngsaiii_inter = solution.objectives[1]
ngsaiii_stress = solution.objectives[0]
val = count
count += 1
sample = out[result2[val].objectives]
x = sample[0:][::2]
y = sample[1:][::2]
newD = pd.DataFrame({"x1": x, "x2": y})
print "Stress for NGSAIII = ", ngsaiii_stress
print "Total Intersection NGSAIII = ", ngsaiii_inter
graph(newD, d2, m3, number, 3, ngsaiii_stress, ngsaiii_inter)
def NSGAIII_Experiment():
NSGAIII_results = {}
hypNS3 = [0]
random.seed(0)
for d in range(1, 10):
hyp = []
nfes = []
for i in range(100):
problem = Problem(7, 2, 11)
problem.types[:] = [
Real(2.6, 3.6),
Real(0.7, 0.8),
Real(17, 28),
Real(7.3, 8.3),
Real(7.3, 8.3),
Real(2.9, 3.9),
Real(5, 5.5)
]
problem.constraints[:] = "<=0"
problem.function = SRD
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'SRD'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([7000, 1700])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 20):
hyp = []
nfes = []
for i in range(100):
problem = Problem(3, 2, 3)
problem.types[:] = [
Real(1, 3), Real(0.0005, 0.05),
Real(0.0005, 0.05)
]
problem.constraints[:] = "<=0"
problem.function = TBTD
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'TBTD'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([0.1, 50000])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 30):
hyp = []
nfes = []
for i in range(100):
problem = Problem(4, 2, 5)
problem.types[:] = [
Real(0.125, 5),
Real(0.1, 10),
Real(0.1, 10),
Real(0.125, 5)
]
problem.constraints[:] = "<=0"
problem.function = WB
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'WB'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([350, 0.1])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 30):
hyp = []
nfes = []
for i in range(100):
problem = Problem(4, 2, 5)
problem.types[:] = [
Real(55, 80),
Real(75, 110),
Real(1000, 3000),
Real(2, 20)
]
problem.constraints[:] = "<=0"
problem.function = DBD
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'DBD'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([5, 50])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 20):
hyp = []
nfes = []
for i in range(100):
problem = Problem(2, 2, 5)
problem.types[:] = [Real(20, 250), Real(10, 50)]
problem.constraints[:] = "<=0"
problem.function = NBP
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'NBP'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([11150, 12500])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 10):
hyp = []
nfes = []
for i in range(100):
problem = Problem(6, 3, 9)
problem.types[:] = [
Real(150, 274.32),
Real(25, 32.31),
Real(12, 22),
Real(8, 11.71),
Real(14, 18),
Real(0.63, 0.75)
]
problem.constraints[:] = "<=0"
problem.function = SPD
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'SPD'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([16, 19000, -260000])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 20):
hyp = []
nfes = []
for i in range(100):
problem = Problem(7, 3, 10)
problem.types[:] = [
Real(0.5, 1.5),
Real(0.45, 1.35),
Real(0.5, 1.5),
Real(0.5, 1.5),
Real(0.875, 2.625),
Real(0.4, 1.2),
Real(0.4, 1.2)
]
problem.constraints[:] = "<=0"
problem.function = CSI
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'CSI'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([42, 4.5, 13])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 10):
hyp = []
nfes = []
for i in range(100):
problem = Problem(3, 5, 7)
problem.types[:] = [
Real(0.01, 0.45),
Real(0.01, 0.1),
Real(0.01, 0.1)
]
problem.constraints[:] = "<=0"
problem.function = WP
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'WP'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([83000, 1350, 2.85, 15989825, 25000])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 20):
hyp = []
nfes = []
for i in range(1):
problem = Problem(2, 2, 2)
problem.types[:] = [Real(0, 5), Real(0, 3)]
problem.constraints[:] = "<=0"
problem.function = BNH
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'BNH'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([140, 50])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(d)
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 20):
hyp = []
nfes = []
for i in range(100):
problem = Problem(2, 2, 2)
problem.types[:] = [Real(0.1, 1), Real(0, 5)]
problem.constraints[:] = "<=0"
problem.function = CEXP
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'CEXP'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([1, 9])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 100):
hyp = []
nfes = []
for i in range(100):
problem = Problem(6, 2, 2)
problem.types[:] = [
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1)
]
problem.constraints[:] = "<=0"
problem.function = C3DTLZ4
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'C3DTLZ4'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([3, 3])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 40):
hyp = []
nfes = []
for i in range(100):
problem = Problem(2, 2, 2)
problem.types[:] = [Real(-20, 20), Real(-20, 20)]
problem.constraints[:] = "<=0"
problem.function = SRN
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'SRN'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([301, 72])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 40):
hyp = []
nfes = []
for i in range(100):
problem = Problem(2, 2, 2)
problem.types[:] = [Real(1e-5, np.pi), Real(1e-5, np.pi)]
problem.constraints[:] = "<=0"
problem.function = TNK
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'TNK'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([3, 3])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 10):
hyp = []
nfes = []
for i in range(100):
problem = Problem(6, 2, 6)
problem.types[:] = [
Real(0, 10),
Real(0, 10),
Real(1, 5),
Real(0, 6),
Real(1, 5),
Real(0, 10)
]
problem.constraints[:] = "<=0"
problem.function = OSY
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'OSY'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([0, 386])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 40):
hyp = []
nfes = []
for i in range(100):
problem = Problem(2, 2, 2)
problem.types[:] = [Real(0, 1), Real(0, 1)]
problem.constraints[:] = "<=0"
problem.function = CTP1
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'CTP1'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([1, 2])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 20):
hyp = []
nfes = []
for i in range(100):
problem = Problem(10, 2, 1)
problem.types[:] = [
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1)
]
problem.constraints[:] = "<=0"
problem.function = BICOP1
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'BICOP1'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([9, 9])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 100):
hyp = []
nfes = []
for i in range(100):
problem = Problem(10, 2, 2)
problem.types[:] = [
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1),
Real(0, 1)
]
problem.constraints[:] = "<=0"
problem.function = BICOP2
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'BICOP2'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([70, 70])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
hypNS3 = [0]
random.seed(0)
for d in range(1, 10):
hyp = []
nfes = []
for i in range(100):
problem = Problem(2, 3, 3)
problem.types[:] = [Real(-4, 4), Real(-4, 4)]
problem.constraints[:] = "<=0"
problem.function = TRICOP
algorithm = NSGAIII(problem, d)
algorithm.run(40 * problem.nvars)
funcname = 'TRICOP'
nondominated_solutions = nondominated(algorithm.result)
ref = np.array([34, -4, 90])
obj = []
for s in nondominated_solutions:
lijst = str(s.objectives)
obj.append(ast.literal_eval(lijst))
obj = np.array(obj)
hyp.append(hypervolume(obj, ref))
nfes.append(algorithm.nfe)
if np.mean(nfes) < (40 * problem.nvars * 1.1):
if np.mean(hypNS3) < np.mean(hyp):
hypNS3 = hyp
print(funcname, np.mean(hypNS3), '(', np.std(hypNS3), ')')
NSGAIII_results[funcname] = hypNS3
return NSGAIII_results
from platypus import NSGAIII, DTLZ2
# define the problem definition
problem = DTLZ2(3)
# instantiate the optimization algorithm
algorithm = NSGAIII(problem, divisions_outer=12)
# optimize the problem using 10,000 function evaluations
algorithm.run(10000)
# plot the results using matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter([s.objectives[0] for s in algorithm.result],
[s.objectives[1] for s in algorithm.result],
[s.objectives[2] for s in algorithm.result])
plt.show()
def moea(name,
solsize,
popsize,
wscalar_,
moea_type,
max_gen=float('inf'),
timeLimit=float('inf')):
from platypus import Problem, TournamentSelector
from platypus import NSGAII, NSGAIII, SPEA2
from platyplus.operators import varOr, mutGauss, cxUniform
from platyplus.types import RealGauss
from platyplus.algorithms import SMSEMOA
N = wscalar_.xdim
M = wscalar_.M
time_start = time.perf_counter()
logger.info('Running ' + moea_type + ' in ' + name)
prMutation = 0.1
prVariation = 1 - prMutation
vartor = varOr(cxUniform(), mutGauss(), prVariation, prMutation)
def eval_(theta):
return wscalar_.f(np.array(theta))
problem = Problem(N, M)
problem.types[:] = [RealGauss() for i in range(N)]
problem.function = eval_
if moea_type == 'NSGAII':
alg = NSGAII(problem,
population_size=popsize,
selector=TournamentSelector(1),
variator=vartor)
elif moea_type == 'NSGAIII':
alg = NSGAIII(problem,
divisions_outer=3,
population_size=popsize,
selector=TournamentSelector(1),
variator=vartor)
elif moea_type == 'SPEA2':
alg = SPEA2(problem,
population_size=popsize,
selector=TournamentSelector(1),
variator=vartor)
elif moea_type == 'SMSdom':
alg = SMSEMOA(problem,
population_size=popsize,
selector=TournamentSelector(1),
variator=vartor,
selection_method='nbr_dom')
elif moea_type == 'SMShv':
alg = SMSEMOA(problem,
population_size=popsize,
selector=TournamentSelector(1),
variator=vartor,
selection_method='hv_contr')
gen = 1
while gen < max_gen and time.perf_counter() - time_start < timeLimit:
alg.step()
gen += 1
alg.population_size = solsize
alg.step()
moeaSols = [eval_(s.variables) for s in alg.result]
moea_time = time.perf_counter() - time_start
logger.info(moea_type + ' in ' + name + ' finnished.')
return moeaSols, moea_time