# Random seed for experimental consistency
np.random.seed(0)
# Number of agents and decision variables
n_agents = 20
n_variables = 2
# Lower and upper bounds (has to be the same size as `n_variables`)
lower_bound = [-10, -10]
upper_bound = [10, 10]
# Creates the space, optimizer and function
space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound)
optimizer = PSO()
function = Function(Sphere())
# Bundles every piece into Opytimizer class
opt = Opytimizer(space, optimizer, function, save_agents=False)
# Runs the optimization task
opt.start(n_iterations=10, callbacks=[CheckpointCallback(frequency=10)])
# Deletes the optimization objecs
del opt
# Loads the task from file and resumes it
# Note that the following lines achieves the same results as a 35-iteration running
opt = Opytimizer.load('iter_10_checkpoint.pkl')
opt.start(n_iterations=25)
# Random seed for experimental consistency
np.random.seed(0)
# Number of agents and decision variables
n_agents = 20
n_variables = 2
# Lower and upper bounds (has to be the same size as `n_variables`)
lower_bound = [-10, -10]
upper_bound = [10, 10]
# Creates the space, optimizer and function
space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound)
optimizer = PSO()
function = Function(Sphere())
# Bundles every piece into Opytimizer class
opt = Opytimizer(space, optimizer, function, save_agents=False)
# Runs the optimization task
opt.start(n_iterations=10, callbacks=[CheckpointCallback(frequency=10)])
# Deletes the optimization objecs
del opt
# Loads the task from file and resumes it
# Note that the following lines achieves the same results as a 35-iteration running
opt = Opytimizer.load("iter_10_checkpoint.pkl")
opt.start(n_iterations=25)
