def __init__(self,
search_space,
fitness_function,
IDW,
noisemap,
x_y_limits,
z_sigma,
work_space,
random_state,
init_network,
sample_point_distance,
restricted_airspace,
flight_constraints,
geofence_point_boundary,
minimization=True):
Problem.__init__(self, search_space, fitness_function, minimization)
self.IDW = IDW
self.noisemap = noisemap
self.x_y_limits = x_y_limits
self.z_sigma = z_sigma
self.work_space = work_space
self.random_state = random_state
self.init_network = init_network
self.sample_point_distance = sample_point_distance
self.endpoints = self._get_endpoints()
self.restricted_airspace = restricted_airspace
self.flight_constraints = flight_constraints
self.geofence_point_boundary = geofence_point_boundary
def __init__(self, **kwargs):
Problem.__init__(self, **kwargs)
self.jobs = {'quantity': 0, 'list': [], 'total_units': []}
self.machines = {'quantity': 0, 'loadout_times': [], 'lower_bounds_taillard': [], 'assigned_jobs': []}
# Load benchmark instance
self.ilb = 0 # Instance lower bound
self.iub = 0 # Instance upper bound
self.load_instance()
# Set n dimensions
self.n = self.jobs['quantity']
self.pre_processing_done = False
def __init__(
self, decision_vector, objective_vector, solver, highs, lows, directions=None, is_empty=False, **settings
):
Problem.__init__(self)
if is_empty:
return
if not directions:
directions = [True] * len(objective_vector)
self.decisions = [Decision(dec.decl().name(), is_true(False), is_true(True)) for dec in decision_vector]
self.objectives = [
Objective(obj.decl().name(), directions[i], lows[i], highs[i]) for i, obj in enumerate(objective_vector)
]
self.decision_vector = decision_vector
self.objective_vector = objective_vector
self.solver = solver
self.base_solver = clone(solver)
self.generation_counter = 0
def __init__(self, **kwargs):
Problem.__init__(self, **kwargs)
# Set n dimensions
self.n = 2