def __init__(self, optimizer, problems, num_runs, max_evaluations, weights=None):
"""
Create object instance.
:param optimizer: Optimizer-class, e.g. PSO, MOL or DE.
:param problems: List of instances of the Problem-class.
:param num_runs: Number of optimization runs to perform for each problem.
:param max_evaluations: Number of fitness evaluations for each optimization run.
:param weights: List of weights for the problems to adjust their mutual importance.
:return: Object instance.
"""
# Copy arguments to instance variables.
self.optimizer = optimizer
self.num_runs = num_runs
self.max_evaluations = max_evaluations
# Wrap the problems and weights. This is used for ranking the problems
# which significantly speeds up the execution time, as explained below.
if weights is None:
# No weights were supplied so we just wrap the problems.
self.problem_ranks = [_ProblemRank(problem) for problem in problems]
else:
# Wrap both the problems and weights.
self.problem_ranks = [_ProblemRank(problem, weight) for problem, weight in zip(problems, weights)]
# The MetaFitness-class is actually an optimization problem,
# so init the parent-class.
# The dimensionality of the search-space is the number of
# control parameters for the optimizer, and the search-space
# boundaries are the boundaries for the control parameters.
Problem.__init__(self, name="MetaFitness",
dim=optimizer.num_parameters, fitness_min=0.0,
lower_bound=optimizer.parameters_lower_bound,
upper_bound=optimizer.parameters_upper_bound)
def __init__(self, optimizer, problems, num_runs, max_evaluations, weights=None):
"""
Create object instance.
:param optimizer: Optimizer-class, e.g. PSO, MOL or DE.
:param problems: List of instances of the Problem-class.
:param num_runs: Number of optimization runs to perform for each problem.
:param max_evaluations: Number of fitness evaluations for each optimization run.
:param weights: List of weights for the problems to adjust their mutual importance.
:return: Object instance.
"""
# Copy arguments to instance variables.
self.optimizer = optimizer
self.num_runs = num_runs
self.max_evaluations = max_evaluations
# Wrap the problems and weights. This is used for ranking the problems
# which significantly speeds up the execution time, as explained below.
if weights is None:
# No weights were supplied so we just wrap the problems.
self.problem_ranks = [_ProblemRank(problem) for problem in problems]
else:
# Wrap both the problems and weights.
self.problem_ranks = [_ProblemRank(problem, weight) for problem, weight in zip(problems, weights)]
# The MetaFitness-class is actually an optimization problem,
# so init the parent-class.
# The dimensionality of the search-space is the number of
# control parameters for the optimizer, and the search-space
# boundaries are the boundaries for the control parameters.
Problem.__init__(self, name="MetaFitness",
dim=optimizer.num_parameters, fitness_min=0.0,
lower_bound=optimizer.parameters_lower_bound,
upper_bound=optimizer.parameters_upper_bound)
eta_q= np.array([-0.250 , -0.250]) * 2 * np.pi
parameter = [frequency,coupling,eta_q]
QB = Qubits(qubits_parameter = parameter)
ZZ = QB.E_eig[QB._findstate('11')]-QB.E_eig[QB._findstate('01')]-QB.E_eig[QB._findstate('10')]+QB.E_eig[QB._findstate('00')]
# swarmops
func = partial(getfid , QB = QB , parallel = False , limit = np.Infinity)
lower_bound = [20 , 0.02 , (5.2-0.152) ]
upper_bound = [150 , 0.40 , (5.2-0.148) ]
lower_init=[20 , 0.1 , (5.2-0.152) ]
upper_init=[90 , 0.2 , (5.2-0.148) ]
problem = Problem(name="CNOT_OPT_"+str(index), dim=3, fitness_min=0.0,
lower_bound=lower_bound,
upper_bound=upper_bound,
lower_init=lower_init,
upper_init=upper_init,
func=func)
print('start')
optimizer = SuSSADE
parameters = [20, 0.3, 0.9 , 0.9 ]
# Start a timer.
timer = Timer()
# Perform a single optimization run using the optimizer
# where the fitness is evaluated in parallel.
result = optimizer(parallel=True, problem=problem,
max_evaluations=500,
display_interval=1,
# P = [65.12955 , 0.034855 , 5.049587 , -0.135331 , 0.319984 , 0]
# fid = getfid(P , parallel = True)
# print(1-fid)
# swarmops
func = partial(getfid, parallel=False, limit=np.Infinity)
lower_bound = [40, 0.02, (5.2 - 0.152), -np.pi, -np.pi]
upper_bound = [160, 0.15, (5.2 - 0.148), np.pi, np.pi]
lower_init = [40, 0.02, (5.2 - 0.152), -np.pi, -np.pi]
upper_init = [160, 0.15, (5.2 - 0.148), np.pi, np.pi]
problem = Problem(name="CNOT_OPT",
dim=5,
fitness_min=0.0,
lower_bound=lower_bound,
upper_bound=upper_bound,
lower_init=lower_init,
upper_init=upper_init,
func=func)
print('start')
optimizer = SuSSADE
parameters = [20, 0.3, 0.9, 0.9]
# Start a timer.
timer = Timer()
# Perform a single optimization run using the optimizer
# where the fitness is evaluated in parallel.
result = optimizer(parallel=True,
problem=problem,
# Max number of fitness evaluations.
max_evaluations = dim * 10
# Number of fitness evaluations between printing of status messages.
display_interval = 1
# Max-length of fitness trace.
trace_len = 100
# Create object-instance for a benchmark problem.
#problem = Problem.Ackley(dim=dim)
#problem = Problem.Sphere(dim=dim)
#problem = Problem.Griewank(dim=dim)
#problem = Problem.Rastrigin(dim=dim)
problem = Problem.Rosenbrock(dim=dim)
#problem = Problem.Schwefel1_2(dim=dim)
#problem = Problem.Schwefel2_21(dim=dim)
#problem = Problem.Schwefel2_22(dim=dim)
#problem = Problem.Step(dim=dim)
#problem = Problem.QuarticNoise(dim=dim)
#problem = Problem.Penalized1(dim=dim)
#problem = Problem.Penalized2(dim=dim)
# Example of how to wrap a function in a problem-object.
if False:
problem = Problem.Benchmark(name="Sphere Wrap Func", dim=dim, fitness_min=0.0,
lower_bound=-100.0, upper_bound=100.0,
lower_init=50.0, upper_init=100.0,
func=sphere_func)