def main():
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_matlab_engine.log"):
os.remove("./logfiles/test_matlab_engine.log")
logging.basicConfig(filename="./logfiles/test_matlab_engine.log",
level=logging.INFO)
print("\nNumber of threads: 4")
print("Maximum number of evaluations: 500")
print("Sampling method: CandidateDYCORS")
print("Experimental design: Latin Hypercube")
print("Surrogate: Cubic RBF")
nthreads = 4
maxeval = 500
data = Ackley(dim=10)
print(data.info)
# Use the serial controller (uses only one thread)
controller = ThreadController()
controller.strategy = \
SyncStrategyNoConstraints(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nthreads,
exp_design=LatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail,
maxp=maxeval),
sampling_method=CandidateDYCORS(data=data, numcand=100*data.dim))
print("\nNOTE: You may need to specify the matlab_root keyword in "
"order \n to start a MATLAB session using the matlab_wrapper "
"module\n")
# We need to tell MATLAB where the script is
mfile_location = os.getcwd()
# Launch the threads
for _ in range(nthreads):
worker = MatlabWorker(controller)
try:
worker.matlab = matlab_wrapper.MatlabSession(options='-nojvm')
except Exception as err:
print("\nERROR: Failed to initialize a MATLAB session.\n")
exit()
worker.matlab.workspace.addpath(mfile_location)
controller.launch_worker(worker)
# Run the optimization strategy
result = controller.run()
# Print the final result
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
def solve_opf_dycors_parallel(problem: AcOPFBlackBox,
maxeval=1000,
nthreads=4,
verbose=False):
"""
:param problem:
:return:
"""
print(problem.info)
# (2) Experimental design
# Use a symmetric Latin hypercube with 2d + 1 samples
exp_des = SymmetricLatinHypercube(dim=problem.dim,
npts=2 * problem.dim + 1)
# (3) Surrogate model
# Use a cubic RBF interpolant with a linear tail
surrogate = RBFInterpolant(kernel=CubicKernel,
tail=LinearTail,
maxp=maxeval)
# (4) Adaptive sampling
# Use DYCORS with 100d candidate points
adapt_samp = CandidateDYCORS(data=problem, numcand=100 * problem.dim)
# Use the threaded controller
controller = ThreadController()
# (5) Use the sychronous strategy without non-bound constraints
# Use 4 threads and allow for 4 simultaneous evaluations
strategy = SyncStrategyNoConstraints(worker_id=0,
data=problem,
maxeval=maxeval,
nsamples=nthreads,
exp_design=exp_des,
response_surface=surrogate,
sampling_method=adapt_samp)
controller.strategy = strategy
# Launch the threads and give them access to the objective function
for _ in range(nthreads):
worker = BasicWorkerThread(controller, problem.objfunction)
controller.launch_worker(worker)
# Run the optimization strategy
result = controller.run()
# Print the final result
if verbose:
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
return result.value
def optimization_trial(pname, data, epsilon, nthreads, maxeval, num):
nsamples = nthreads
print("Trial Number:" + str(num))
# Create a strategy and a controller
controller = ThreadController()
controller.strategy = \
MoSyncStrategyNoConstraints(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nsamples,
exp_design=SymmetricLatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail,
maxp=maxeval),
sampling_method=EvolutionaryAlgorithm(data,epsilons=epsilon, cand_flag=1),
archiving_method=EpsilonArchive(size_max=200,epsilon=epsilon))
# Launch the threads and give them access to the objective function
for _ in range(nthreads):
worker = BasicWorkerThread(controller, data.objfunction)
controller.launch_worker(worker)
# Run the optimization strategy
def merit(r):
return r.value[0]
result = controller.run(merit=merit)
# Save results to File
X = np.loadtxt('final.txt')
controller.strategy.save_plot(num)
fname = pname + '_' + str(data.dim) + '_EGOMORS_' + str(maxeval) + '_' + str(num) + '_' + str(nthreads) + '.txt'
np.savetxt(fname, X)
def solve_opf_dycors_serial(problem: AcOPFBlackBox,
maxeval=1000,
verbose=False,
stop_at=False,
stop_value=0):
"""
:param problem:
:param maxeval:
:param verbose:
:param stop_at:
:param stop_value:
:return:
"""
print(problem.info)
# (2) Experimental design
# Use a symmetric Latin hypercube with 2d + 1 samples
exp_des = SymmetricLatinHypercube(dim=problem.dim,
npts=2 * problem.dim + 1)
# (3) Surrogate model
# Use a cubic RBF interpolant with a linear tail
surrogate = RBFInterpolant(kernel=CubicKernel,
tail=LinearTail,
maxp=maxeval)
# (4) Adaptive sampling
# Use DYCORS with 100d candidate points
adapt_samp = CandidateDYCORS(data=problem, numcand=100 * problem.dim)
# Use the serial controller (uses only one thread)
controller = SerialController(problem.objfunction)
# (5) Use the sychronous strategy without non-bound constraints
strategy = SyncStrategyNoConstraints(worker_id=0,
data=problem,
maxeval=maxeval,
nsamples=1,
exp_design=exp_des,
response_surface=surrogate,
sampling_method=adapt_samp)
controller.strategy = strategy
# Run the optimization strategy
result = controller.run(stop_at=stop_at, stop_value=stop_value)
# Print the final result
if verbose:
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
# the result is x
return result.value, result.params[0]
def main():
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_penalty.log"):
os.remove("./logfiles/test_penalty.log")
logging.basicConfig(filename="./logfiles/test_penalty.log",
level=logging.INFO)
print("\nNumber of threads: 4")
print("Maximum number of evaluations: 500")
print("Sampling method: CandidateDYCORS")
print("Experimental design: Symmetric Latin Hypercube")
print("Surrogate: Cubic RBF")
nthreads = 4
maxeval = 500
penalty = 1e6
nsamples = nthreads
data = Keane(dim=10)
print(data.info)
# Create a strategy and a controller
controller = ThreadController()
controller.strategy = \
SyncStrategyPenalty(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nsamples,
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail, maxp=maxeval),
exp_design=SymmetricLatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
sampling_method=CandidateDYCORS(data=data, numcand=100*data.dim),
penalty=penalty)
# Launch the threads
for _ in range(nthreads):
worker = BasicWorkerThread(controller, data.objfunction)
controller.launch_worker(worker)
# Use penalty based merit
def feasible_merit(record):
xx = np.zeros((1, record.params[0].shape[0]))
xx[0, :] = record.params[0]
return record.value + controller.strategy.penalty_fun(xx)[0, 0]
result = controller.run(merit=feasible_merit)
best, xbest = result.value, result.params[0]
print('Best value: {0}'.format(best))
print('Best solution: {0}'.format(
np.array_str(xbest,
max_line_width=np.inf,
precision=5,
suppress_small=True)))
print('Feasible: {0}\n'.format(
np.max(data.eval_ineq_constraints(xbest)) <= 0.0))
def main():
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_projection.log"):
os.remove("./logfiles/test_projection.log")
logging.basicConfig(filename="./logfiles/test_projection.log",
level=logging.INFO)
print("\nNumber of threads: 4")
print("Maximum number of evaluations: 1000")
print("Sampling method: CandidateDYCORS")
print("Experimental design: Latin Hypercube")
print("Surrogate: Cubic RBF")
nthreads = 4
maxeval = 1000
nsamples = nthreads
data = AckleyUnit(dim=10)
print(data.info)
def projection(x):
return x / np.linalg.norm(x)
# Create a strategy and a controller
controller = ThreadController()
controller.strategy = \
SyncStrategyProjection(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nsamples,
exp_design=LatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail, maxp=maxeval),
sampling_method=CandidateDYCORS(data=data, numcand=100*data.dim),
proj_fun=projection
)
# Launch the threads and give them access to the objective function
for _ in range(nthreads):
worker = BasicWorkerThread(controller, data.objfunction)
controller.launch_worker(worker)
# Run the optimization strategy
result = controller.run()
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
print('||x||_2 = {0}\n'.format(np.linalg.norm(result.params[0])))
def main():
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_subprocess_files.log"):
os.remove("./logfiles/test_subprocess_files.log")
logging.basicConfig(filename="./logfiles/test_subprocess_files.log",
level=logging.INFO)
print("\nNumber of threads: 4")
print("Maximum number of evaluations: 200")
print("Sampling method: Candidate DYCORS")
print("Experimental design: Symmetric Latin Hypercube")
print("Surrogate: Cubic RBF")
assert os.path.isfile("./sphere_ext_files"), "You need to build sphere_ext"
nthreads = 4
maxeval = 200
nsamples = nthreads
data = Sphere(dim=10)
print(data.info)
# Create a strategy and a controller
controller = ThreadController()
controller.strategy = \
SyncStrategyNoConstraints(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nsamples,
exp_design=SymmetricLatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
sampling_method=CandidateDYCORS(data=data, numcand=100*data.dim),
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail,
maxp=maxeval))
# Launch the threads and give them access to the objective function
for i in range(nthreads):
worker = CppSim(controller)
worker.my_filename = str(i) + ".txt"
controller.launch_worker(worker)
# Run the optimization strategy
result = controller.run()
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
def main():
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_simple_time.log"):
os.remove("./logfiles/test_simple_time.log")
logging.basicConfig(filename="./logfiles/test_simple_time.log",
level=logging.INFO)
print("\nNumber of threads: 4")
print("Time budget: 30 seconds")
print("Sampling method: CandidateDYCORS")
print("Experimental design: Symmetric Latin Hypercube")
print("Surrogate: Cubic RBF")
nthreads = 4
maxeval = -30
nsamples = nthreads
data = Ackley(dim=10)
print(data.info)
# Create a strategy and a controller
controller = ThreadController()
controller.strategy = \
SyncStrategyNoConstraints(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nsamples,
exp_design=SymmetricLatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail,
maxp=1000),
sampling_method=CandidateDYCORS(data=data, numcand=100*data.dim))
# Launch the threads and give them access to the objective function
for _ in range(nthreads):
worker = BasicWorkerThread(controller, data.objfunction)
controller.launch_worker(worker)
# Run the optimization strategy
start_time = time.time()
result = controller.run()
end_time = time.time()
print('Run time: {0} seconds'.format(end_time - start_time))
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0], max_line_width=np.inf,
precision=5, suppress_small=True)))
def main():
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_multisampling.log"):
os.remove("./logfiles/test_multisampling.log")
logging.basicConfig(filename="./logfiles/test_multisampling.log",
level=logging.INFO)
print("\nNumber of threads: 1")
print("Maximum number of evaluations: 500")
print(
"Sampling method: CandidateDYCORS, Genetic Algorithm, Multi-Start Gradient"
)
print("Experimental design: Latin Hypercube")
print("Surrogate: Cubic RBF")
nthreads = 1
maxeval = 500
nsamples = nthreads
data = Ackley(dim=10)
print(data.info)
# Create a strategy and a controller
sampling_method = [
CandidateDYCORS(data=data, numcand=100 * data.dim),
GeneticAlgorithm(data=data),
MultiStartGradient(data=data)
]
controller = SerialController(data.objfunction)
controller.strategy = \
SyncStrategyNoConstraints(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nsamples,
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail, maxp=maxeval),
exp_design=SymmetricLatinHypercube(dim=data.dim, npts=2*(data.dim + 1)),
sampling_method=MultiSampling(sampling_method, [0, 1, 0, 2]))
result = controller.run()
best, xbest = result.value, result.params[0]
print('Best value: {0}'.format(best))
print('Best solution: {0}\n'.format(
np.array_str(xbest,
max_line_width=np.inf,
precision=5,
suppress_small=True)))
def main():
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_simple.log"):
os.remove("./logfiles/test_simple.log")
logging.basicConfig(filename="./logfiles/test_simple.log",
level=logging.INFO)
nthreads = 4
maxeval = 100
nsamples = nthreads
print("\nNumber of threads: " + str(nthreads))
print("Maximum number of evaluations: " + str(maxeval))
print("Sampling method: Mixed")
print("Experimental design: Symmetric Latin Hypercube")
print("Surrogate: Cubic RBF")
#data = LZF3()
data = DTLZ4(nobj=2)
num = 1
epsilons = [0.05, 0.05]
# Create a strategy and a controller
controller = ThreadController()
#controller = SerialController(data.objfunction)
controller.strategy = \
MoSyncStrategyNoConstraints(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nsamples,
exp_design=SymmetricLatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail,
maxp=maxeval),
sampling_method=EvolutionaryAlgorithm(data,epsilons=epsilons, cand_flag=1), archiving_method=EpsilonArchive(size_max=200,epsilon=epsilons))
# Launch the threads and give them access to the objective function
for _ in range(nthreads):
worker = BasicWorkerThread(controller, data.objfunction)
controller.launch_worker(worker)
# Run the optimization strategy
def merit(r):
return r.value[0]
result = controller.run(merit=merit)
controller.strategy.save_plot(num)
def main_master(nworkers):
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_subprocess_mpi.log"):
os.remove("./logfiles/test_subprocess_mpi.log")
logging.basicConfig(filename="./logfiles/test_subprocess_mpi.log",
level=logging.INFO)
print(
"\nTesting the POAP MPI controller with {0} workers".format(nworkers))
print("Maximum number of evaluations: 200")
print("Search strategy: Candidate DYCORS")
print("Experimental design: Symmetric Latin Hypercube")
print("Surrogate: Cubic RBF")
assert os.path.isfile("./sphere_ext"), "You need to build sphere_ext"
maxeval = 200
data = Sphere(dim=10)
print(data.info)
# Create a strategy and a controller
strategy = \
SyncStrategyNoConstraints(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nworkers,
exp_design=SymmetricLatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
sampling_method=CandidateDYCORS(data=data, numcand=100*data.dim),
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail,
maxp=maxeval))
controller = MPIController(strategy)
# Run the optimization strategy
result = controller.run()
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
"""
if self.weights is None:
self.compute_weights()
val = 0.0
for i in range(self.M):
val += self.weights[i] * self.model_list[i].deriv(x, d)
return val
if __name__ == "__main__":
from pySOT import RBFInterpolant
from pySOT import CubicRBFSurface, TPSSurface, LinearRBFSurface
fhat1 = RBFInterpolant(CubicRBFSurface, 1e-8, 100)
fhat2 = RBFInterpolant(TPSSurface, 1e-8, 100)
fhat3 = RBFInterpolant(LinearRBFSurface, 1e-8, 100)
models = [fhat1, fhat2, fhat3]
fhat = EnsembleSurrogate(models, 10)
def test_f(x):
"""Test function"""
fx = x[1] * np.sin(x[0]) + x[0] * np.cos(x[1])
return fx
def test_df(x):
"""Derivative of test function"""
dfx = np.array([
x[1] * np.cos(x[0]) + np.cos(x[1]),
"""
if self.weights is None:
self.compute_weights()
val = 0.0
for i in range(self.M):
val += self.weights[i] * self.model_list[i].deriv(x, d)
return val
if __name__ == "__main__":
from pySOT import RBFInterpolant
from pySOT import CubicKernel, TPSKernel, LinearKernel, LinearTail, ConstantTail
fhat1 = RBFInterpolant(CubicKernel, LinearTail, 100, 1e-8)
fhat2 = RBFInterpolant(TPSKernel, LinearTail, 100, 1e-8)
fhat3 = RBFInterpolant(LinearKernel, ConstantTail, 100, 1e-8)
models = [fhat1, fhat2, fhat3]
fhat = EnsembleSurrogate(models, 10)
def test_f(x):
"""Test function"""
fx = x[1] * np.sin(x[0]) + x[0] * np.cos(x[1])
return fx
def test_df(x):
"""Derivative of test function"""
dfx = np.array([
x[1] * np.cos(x[0]) + np.cos(x[1]),
def main():
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_ensemble.log"):
os.remove("./logfiles/test_ensemble.log")
logging.basicConfig(filename="./logfiles/test_ensemble.log",
level=logging.INFO)
print("\nNumber of threads: 5")
print("Maximum number of evaluations: 250")
print("Sampling method: CandidateSRBF")
print("Experimental design: Symmetric Latin Hypercube + point [1,1,...,1]")
print("Ensemble Surrogate: Cubic RBF, PolyReg")
nthreads = 5
maxeval = 250
nsamples = nthreads
data = Ackley(dim=5)
print(data.info)
# Use RBF + PolyReg
bounds = np.vstack((data.xlow, data.xup)).T
basisp = basis_TD(data.dim, 2) # use order 2
models = [
RBFInterpolant(kernel=CubicKernel, tail=LinearTail, maxp=maxeval),
PolyRegression(bounds, basisp)
]
response_surface = EnsembleSurrogate(model_list=models, maxp=maxeval)
# Add an additional point to the experimental design. If a good
# solution is already known you can add this point to the
# experimental design
extra = np.ones((1, data.dim))
# Create a strategy and a controller
controller = ThreadController()
controller.strategy = \
SyncStrategyNoConstraints(
worker_id=0, data=data,
response_surface=response_surface,
maxeval=maxeval, nsamples=nsamples,
exp_design=SymmetricLatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
sampling_method=CandidateSRBF(data=data, numcand=100*data.dim),
extra=extra)
# Launch the threads and give them access to the objective function
for _ in range(nthreads):
worker = BasicWorkerThread(controller, data.objfunction)
controller.launch_worker(worker)
# Run the optimization strategy
result = controller.run()
response_surface.compute_weights()
print('Final weights: {0}'.format(
np.array_str(response_surface.weights,
max_line_width=np.inf,
precision=5,
suppress_small=True)))
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
def main():
data = Sphere(dim=1)
maxeval = 100
kernel = CubicKernel
tail = LinearTail
for i in range(2):
rbf1 = RBFInterpolant(kernel=kernel, tail=tail, maxp=maxeval, eta=0)
rbf2 = RBFInterpolant(kernel=kernel, tail=tail, maxp=maxeval, eta='adapt')
X = np.random.uniform(data.xlow, data.xup, (maxeval, data.dim))
fX = np.zeros((maxeval, data.dim))
for j in range(maxeval):
fX[j] = data.objfunction(X[j, :])
if i == 0:
fX[j] += 3*np.cos(10000*X[j, :])
rbf1.add_point(X[j, :], fX[j])
rbf2.add_point(X[j, :], fX[j])
Xpred = np.atleast_2d(np.linspace(data.xlow, data.xup, 1000)).transpose()
fXpred = np.zeros((1000, data.dim))
for j in range(1000):
fXpred[j] = data.objfunction(Xpred[j, :])
if i == 0:
print("\nL2 error interpolation with noise: {0}".format(np.linalg.norm(rbf1.evals(Xpred) - fXpred)))
print("L2 error regularization with noise: {0}".format(np.linalg.norm(rbf2.evals(Xpred) - fXpred)))
else:
print("L2 error interpolation without noise: {0}".format(np.linalg.norm(rbf1.evals(Xpred) - fXpred)))
print("L2 error regularization without noise: {0}\n".format(np.linalg.norm(rbf2.evals(Xpred) - fXpred)))
"""
def main():
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_extra_vals.log"):
os.remove("./logfiles/test_extra_vals.log")
logging.basicConfig(filename="./logfiles/test_extra_vals.log",
level=logging.INFO)
print("\nNumber of threads: 4")
print("Maximum number of evaluations: 500")
print("Sampling method: CandidateDYCORS")
print("Experimental design: Symmetric Latin Hypercube")
print("Surrogate: Cubic RBF")
nthreads = 4
maxeval = 500
nsamples = nthreads
data = Ackley(dim=10)
print(data.info)
nextra = 10
extra = np.random.uniform(data.xlow, data.xup, (nextra, data.dim))
extra_vals = np.nan * np.ones((nextra, 1))
for i in range(nextra): # Evaluate every second point
if i % 2 == 0:
extra_vals[i] = data.objfunction(extra[i, :])
# Create a strategy and a controller
controller = ThreadController()
controller.strategy = \
SyncStrategyNoConstraints(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nsamples,
exp_design=SymmetricLatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail,
maxp=maxeval),
sampling_method=CandidateDYCORS(data=data, numcand=100*data.dim),
extra=extra, extra_vals=extra_vals)
# Append the known function values to the POAP database since POAP won't evaluate these points
for i in range(len(extra_vals)):
if not np.isnan(extra_vals[i]):
record = EvalRecord(params=(np.ravel(extra[i, :]), ),
status='completed')
record.value = extra_vals[i]
record.feasible = True
controller.fevals.append(record)
# Launch the threads and give them access to the objective function
for _ in range(nthreads):
worker = BasicWorkerThread(controller, data.objfunction)
controller.launch_worker(worker)
# Run the optimization strategy
result = controller.run()
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
