def optimize_viennet(config, executer, optimize):
if config.getboolean('problem_switch', 'viennet1'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.Viennet1(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'viennet2'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.Viennet2(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'viennet3'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.Viennet3(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'viennet4'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.Viennet4(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
def optimize_zdt_integer(config, executer, optimize):
if config.getboolean('problem_switch', 'zdt5'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.integer.ZDT5(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
def optimize_kursawe(config, executer, optimize):
decisions = config.getint('kursawe', 'decisions')
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.Kursawe(decisions) if decisions > 1 else pyotl.problem.real.Kursawe(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
def optimize_schaffer(config, executer, optimize):
if config.getboolean('problem_switch', 'schaffer1'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.Schaffer1(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'schaffer2'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.Schaffer2(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'schaffer6'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.Schaffer6(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
def optimize_rectangle(config, executer, optimize):
module, function = config.get('rectangle', 'boundaries').rsplit('.', 1)
module = importlib.import_module(module)
for boundary, boundaryOptimal in getattr(module, function)(config):
boundary = pyotl.utility.PyListList2VectorPair_Real(boundary)
boundaryOptimal = pyotl.utility.PyListList2VectorPair_Real(boundaryOptimal)
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.Rectangle(boundary, boundaryOptimal),
lambda optimizer: pyoptimization.problem.fetcher.rectangle(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
def optimize_tsp(config, executer, optimize):
module, function = config.get('tsp', 'matrix').rsplit('.', 1)
module = importlib.import_module(module)
for matrix, city in getattr(module, function)(config):
matrix = pyotl.utility.PyListList2BlasSymmetricMatrix_Real(matrix.tolist())
optimize(config, executer,
lambda **kwargs: pyotl.problem.index.TSP(matrix),
lambda optimizer: pyoptimization.problem.fetcher.tsp(optimizer.GetProblem(),
city) + pyoptimization.problem.fetcher.result.std(
config, optimizer),
)
def optimize_greedy_repair_knapsack(config, executer, optimize):
module, function = config.get('knapsack', 'parameters').rsplit('.', 1)
module = importlib.import_module(module)
for price, weight, capacity in getattr(module, function)(config):
price = pyotl.utility.PyListList2BlasMatrix_Real(price.tolist())
weight = pyotl.utility.PyListList2BlasMatrix_Real(weight.tolist())
capacity = pyotl.utility.PyList2Vector_Real(capacity)
optimize(config, executer,
lambda **kwargs: pyotl.problem.dynamic_bitset.GreedyRepairKnapsack(price, weight, capacity),
lambda optimizer: pyoptimization.problem.fetcher.basic(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.dynamic_bitset(config, optimizer),
)
def optimize_fda(config, executer, optimize):
fixedSteps = config.getint('fda', 'fixed_steps')
distinctSteps = config.getint('fda', 'distinct_steps')
distDecisions = config.getint('fda', 'dist_decisions')
for nObjectives in map(int, config.get('fda', 'objectives').split()):
if config.getboolean('problem_switch', 'fda5'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.FDA5(nObjectives, kwargs['progress'], fixedSteps,
distinctSteps,
distDecisions) if fixedSteps > 0 and distinctSteps > 0 and distDecisions >= 0 else pyotl.problem.real.FDA5(
nObjectives, kwargs['progress']),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
def optimize_zdt_real(config, executer, optimize):
distDecisions = config.getint('zdt', 'dist_decisions')
if config.getboolean('problem_switch', 'zdt1'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ZDT1(
distDecisions) if distDecisions >= 0 else pyotl.problem.real.ZDT1(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'zdt2'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ZDT2(
distDecisions) if distDecisions >= 0 else pyotl.problem.real.ZDT2(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'zdt3'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ZDT3(
distDecisions) if distDecisions >= 0 else pyotl.problem.real.ZDT3(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'zdt4'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ZDT4(
distDecisions) if distDecisions >= 0 else pyotl.problem.real.ZDT4(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'zdt6'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ZDT6(
distDecisions) if distDecisions >= 0 else pyotl.problem.real.ZDT6(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
def optimize_rotated_rectangle(config, executer, optimize):
getDirection = eval(config.get('rotated_rectangle', 'direction'))
module, function = config.get('rotated_rectangle', 'boundaries').rsplit('.', 1)
module = importlib.import_module(module)
for boundary, boundaryOptimal in getattr(module, function)(config):
boundary = pyotl.utility.PyListList2VectorPair_Real(boundary)
boundaryOptimal = pyotl.utility.PyListList2VectorPair_Real(boundaryOptimal)
direction = getDirection(len(boundary))
_direction = pyotl.utility.PyList2BlasVector_Real(direction)
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.RotatedRectangle(boundary, boundaryOptimal, _direction),
lambda optimizer: pyoptimization.problem.fetcher.rotated_rectangle(optimizer.GetProblem(),
direction) + pyoptimization.problem.fetcher.result.std(
config, optimizer),
)
def optimize_onl(config, executer, optimize):
module, function = config.get('onl', 'metrics').rsplit('.', 1)
module = importlib.import_module(module)
metrics = getattr(module, function)(config)
module, function = config.get('community_discovery', 'graphs').rsplit('.', 1)
module = importlib.import_module(module)
for graph in getattr(module, function)(config):
_graph = pyotl.utility.PyListList2BlasSymmetricMatrix_Real(graph.tolist())
for _metrics in metrics:
_metrics = pyotl.problem.community_discovery.PyList2Vector_Metric(_metrics)
optimize(config, executer,
lambda **kwargs: pyotl.problem.index.ONL(_graph, _metrics, kwargs['random']),
lambda optimizer: pyoptimization.problem.fetcher.basic(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
def optimize_water(config, executer, optimize):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.Water(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
def optimize_motsp(config, executer, optimize):
module, function = config.get('motsp', 'matrics').rsplit('.', 1)
module = importlib.import_module(module)
for matrics, city in getattr(module, function)(config):
matrics = [matrix.tolist() for matrix in matrics]
try:
for correlation in map(float, config.get('motsp', 'correlation').split()):
_correlation = [correlation] * (len(matrics) - 1)
_correlation = pyotl.utility.PyList2Vector_Real(_correlation)
_matrics = pyotl.utility.PyListListList2VectorBlasSymmetricMatrix_Real(matrics)
pyotl.problem.index.CorrelateAdjacencyMatrics_Real(_correlation, _matrics)
optimize(config, executer,
lambda **kwargs: pyotl.problem.index.MOTSP(_matrics),
lambda optimizer: pyoptimization.problem.fetcher.correlation_motsp(optimizer.GetProblem(),
city,
correlation) + pyoptimization.problem.fetcher.result.std(
config, optimizer),
)
except configparser.NoOptionError:
_matrics = pyotl.utility.PyListListList2VectorBlasSymmetricMatrix_Real(matrics)
optimize(config, executer,
lambda **kwargs: pyotl.problem.index.MOTSP(_matrics),
lambda optimizer: pyoptimization.problem.fetcher.correlation_motsp(optimizer.GetProblem(), city,
correlation) + pyoptimization.problem.fetcher.result.std(
config, optimizer),
)
def optimize_wfg(config, executer, optimize):
nPosGroups = config.getint('wfg', 'pos_groups')
nDistDecisions = config.getint('wfg', 'dist_decisions')
for nObjectives in map(int, config.get('wfg', 'objectives').split()):
if config.getboolean('problem_switch', 'wfg1'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.WFG1(nObjectives, nPosGroups,
nDistDecisions) if nPosGroups > 0 and nDistDecisions >= 0 else pyotl.problem.real.WFG1(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.wfg(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'wfg2'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.WFG2(nObjectives, nPosGroups,
nDistDecisions) if nPosGroups > 0 and nDistDecisions >= 0 else pyotl.problem.real.WFG2(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.wfg(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'wfg3'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.WFG3(nObjectives, nPosGroups,
nDistDecisions) if nPosGroups > 0 and nDistDecisions >= 0 else pyotl.problem.real.WFG3(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.wfg(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'wfg4'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.WFG4(nObjectives, nPosGroups,
nDistDecisions) if nPosGroups > 0 and nDistDecisions >= 0 else pyotl.problem.real.WFG4(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.wfg(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'wfg5'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.WFG5(nObjectives, nPosGroups,
nDistDecisions) if nPosGroups > 0 and nDistDecisions >= 0 else pyotl.problem.real.WFG5(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.wfg(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'wfg6'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.WFG6(nObjectives, nPosGroups,
nDistDecisions) if nPosGroups > 0 and nDistDecisions >= 0 else pyotl.problem.real.WFG6(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.wfg(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'wfg7'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.WFG7(nObjectives, nPosGroups,
nDistDecisions) if nPosGroups > 0 and nDistDecisions >= 0 else pyotl.problem.real.WFG7(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.wfg(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'wfg8'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.WFG8(nObjectives, nPosGroups,
nDistDecisions) if nPosGroups > 0 and nDistDecisions >= 0 else pyotl.problem.real.WFG8(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.wfg(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'wfg9'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.WFG9(nObjectives, nPosGroups,
nDistDecisions) if nPosGroups > 0 and nDistDecisions >= 0 else pyotl.problem.real.WFG9(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.wfg(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
def optimize_dtlz(config, executer, optimize):
distDecisions = config.getint('dtlz', 'dist_decisions')
for nObjectives in map(int, config.get('dtlz', 'objectives').split()):
if config.getboolean('problem_switch', 'dtlz1'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.DTLZ1(nObjectives,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.DTLZ1(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'dtlz2'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.DTLZ2(nObjectives,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.DTLZ2(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'dtlz3'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.DTLZ3(nObjectives,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.DTLZ3(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'dtlz4'):
fetcher = lambda optimizer: pyoptimization.problem.fetcher.dtlz4(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer)
if distDecisions >= 0:
try:
for baisFactor in map(float, config.get('dtlz4', 'bias_factor').split()):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.DTLZ4(nObjectives, distDecisions, baisFactor),
fetcher,
)
except configparser.NoOptionError:
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.DTLZ4(nObjectives, distDecisions),
fetcher,
)
else:
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.DTLZ4(nObjectives),
fetcher,
)
if config.getboolean('problem_switch', 'dtlz5'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.DTLZ5(nObjectives,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.DTLZ5(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'dtlz6'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.DTLZ6(nObjectives,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.DTLZ6(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'dtlz7'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.DTLZ7(nObjectives,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.DTLZ7(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'dtlz5i'):
for nManifold in eval(config.get('dtlz_i', 'manifold'))(nObjectives):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.DTLZ5I(nObjectives, nManifold,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.DTLZ5I(
nObjectives, nManifold),
lambda optimizer: pyoptimization.problem.fetcher.dtlz_i(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'dtlz6i'):
for nManifold in eval(config.get('dtlz_i', 'manifold'))(nObjectives):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.DTLZ6I(nObjectives, nManifold,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.DTLZ6I(
nObjectives, nManifold),
lambda optimizer: pyoptimization.problem.fetcher.dtlz_i(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'convex_dtlz2'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ConvexDTLZ2(nObjectives,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.ConvexDTLZ2(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'convex_dtlz3'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ConvexDTLZ3(nObjectives,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.ConvexDTLZ3(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'convex_dtlz4'):
lambda optimizer: pyoptimization.problem.fetcher.dtlz4(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer)
if distDecisions >= 0:
try:
for baisFactor in map(float, config.get('dtlz4', 'bias_factor').split()):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ConvexDTLZ4(nObjectives, distDecisions,
baisFactor),
fetcher,
)
except configparser.NoOptionError:
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ConvexDTLZ4(nObjectives, distDecisions),
fetcher,
)
else:
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ConvexDTLZ4(nObjectives),
fetcher,
)
if config.getboolean('problem_switch', 'convex_dtlz5'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ConvexDTLZ5(nObjectives,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.ConvexDTLZ5(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'convex_dtlz6'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ConvexDTLZ6(nObjectives,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.ConvexDTLZ6(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'convex_dtlz5i'):
for nManifold in range(2, nObjectives - 1):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ConvexDTLZ5I(nObjectives, nManifold,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.ConvexDTLZ5I(
nObjectives, nManifold),
lambda optimizer: pyoptimization.problem.fetcher.dtlz_i(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'convex_dtlz6i'):
for nManifold in range(2, nObjectives - 1):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ConvexDTLZ6I(nObjectives, nManifold,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.ConvexDTLZ6I(
nObjectives, nManifold),
lambda optimizer: pyoptimization.problem.fetcher.dtlz_i(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'scaled_dtlz2'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.ScaledDTLZ2(nObjectives,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.ScaledDTLZ2(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'negative_dtlz2'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.NegativeDTLZ2(nObjectives,
distDecisions) if distDecisions >= 0 else pyotl.problem.real.NegativeDTLZ2(
nObjectives),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
def optimize_uf(config, executer, optimize):
distDecisions2 = config.getint('uf', 'dist_decisions2')
distDecisions3 = config.getint('uf', 'dist_decisions3')
if config.getboolean('problem_switch', 'uf1'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.UF1(
distDecisions2) if distDecisions2 >= 0 else pyotl.problem.real.UF1(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'uf2'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.UF2(
distDecisions2) if distDecisions2 >= 0 else pyotl.problem.real.UF2(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'uf3'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.UF3(
distDecisions2) if distDecisions2 >= 0 else pyotl.problem.real.UF3(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'uf4'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.UF4(
distDecisions2) if distDecisions2 >= 0 else pyotl.problem.real.UF4(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'uf5'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.UF5(
distDecisions2) if distDecisions2 >= 0 else pyotl.problem.real.UF5(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'uf6'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.UF6(
distDecisions2) if distDecisions2 >= 0 else pyotl.problem.real.UF6(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'uf7'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.UF7(
distDecisions2) if distDecisions2 >= 0 else pyotl.problem.real.UF7(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'uf8'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.UF8(
distDecisions3) if distDecisions3 >= 0 else pyotl.problem.real.UF8(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'uf9'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.UF9(
distDecisions3) if distDecisions3 >= 0 else pyotl.problem.real.UF9(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)
if config.getboolean('problem_switch', 'uf10'):
optimize(config, executer,
lambda **kwargs: pyotl.problem.real.UF10(
distDecisions3) if distDecisions3 >= 0 else pyotl.problem.real.UF10(),
lambda optimizer: pyoptimization.problem.fetcher.std(
optimizer.GetProblem()) + pyoptimization.problem.fetcher.result.std(config, optimizer),
)