Constrained Optimization

Learn how to optimize problems subject to domain constraints using ConstrainedFunction and penalty factors.

Example Code

import numpy as np
from opytimark.markers.n_dimensional import Sphere

from opytimizer import Opytimizer
from opytimizer.core.stopping import MaxIterations
from opytimizer.functions import ConstrainedFunction
from opytimizer.optimizers.single_objective.swarm import PSO
from opytimizer.spaces import SearchSpace


# Defines a constraint function that returns a boolean
# whether the constraint is valid or not
def c_1(x):
    return x[0] + x[1] < 0


# Random seed for experimental consistency
np.random.seed(0)

# Number of agents and decision variables
n_agents = 20
n_variables = 2
n_objectives = 1

# 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, n_objectives, lower_bound, upper_bound)
optimizer = PSO()
function = ConstrainedFunction(Sphere(), [c_1], penalty=100.0)

# Bundles every piece into Opytimizer class
opt = Opytimizer(space, optimizer, function, save_agents=False)

# Runs the optimization task
opt.start(MaxIterations(1000))