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Analytical test case # 3¶
In this example, we consider a simple optimization problem to illustrate algorithms interfaces and DOE libraries integration. Integer variables are used
Imports¶
from __future__ import annotations
from numpy import sum as np_sum
from gemseo import configure_logger
from gemseo import execute_post
from gemseo.algos.design_space import DesignSpace
from gemseo.algos.doe.doe_factory import DOEFactory
from gemseo.algos.opt_problem import OptimizationProblem
from gemseo.core.mdofunctions.mdo_function import MDOFunction
LOGGER = configure_logger()
Define the objective function¶
We define the objective function \(f(x)=\sum_{i=1}^dx_i\)
using an MDOFunction.
objective = MDOFunction(np_sum, name="f", expr="sum(x)")
Define the design space¶
Then, we define the DesignSpace with GEMSEO.
design_space = DesignSpace()
design_space.add_variable("x", 2, l_b=-5, u_b=5, var_type="integer")
Define the optimization problem¶
Then, we define the OptimizationProblem with GEMSEO.
problem = OptimizationProblem(design_space)
problem.objective = objective
Solve the optimization problem using a DOE algorithm¶
We can see this optimization problem as a trade-off and solve it by means of a design of experiments (DOE), e.g. full factorial design
DOEFactory().execute(problem, "fullfact", n_samples=11**2)
INFO - 08:55:42: Optimization problem:
INFO - 08:55:42: minimize f = sum(x)
INFO - 08:55:42: with respect to x
INFO - 08:55:42: over the design space:
INFO - 08:55:42: +------+-------------+-------+-------------+---------+
INFO - 08:55:42: | Name | Lower bound | Value | Upper bound | Type |
INFO - 08:55:42: +------+-------------+-------+-------------+---------+
INFO - 08:55:42: | x[0] | -5 | None | 5 | integer |
INFO - 08:55:42: | x[1] | -5 | None | 5 | integer |
INFO - 08:55:42: +------+-------------+-------+-------------+---------+
INFO - 08:55:42: Solving optimization problem with algorithm fullfact:
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INFO - 08:55:42: Optimization result:
INFO - 08:55:42: Optimizer info:
INFO - 08:55:42: Status: None
INFO - 08:55:42: Message: None
INFO - 08:55:42: Number of calls to the objective function by the optimizer: 121
INFO - 08:55:42: Solution:
INFO - 08:55:42: Objective: -10.0
INFO - 08:55:42: Design space:
INFO - 08:55:42: +------+-------------+-------+-------------+---------+
INFO - 08:55:42: | Name | Lower bound | Value | Upper bound | Type |
INFO - 08:55:42: +------+-------------+-------+-------------+---------+
INFO - 08:55:42: | x[0] | -5 | -5 | 5 | integer |
INFO - 08:55:42: | x[1] | -5 | -5 | 5 | integer |
INFO - 08:55:42: +------+-------------+-------+-------------+---------+
Post-process the results¶
execute_post(
problem,
"ScatterPlotMatrix",
variable_names=["x", "f"],
save=False,
show=True,
)
<gemseo.post.scatter_mat.ScatterPlotMatrix object at 0x7f1df7686460>
Note that you can get all the optimization algorithms names:
DOEFactory().algorithms
['CustomDOE', 'DiagonalDOE', 'OT_SOBOL', 'OT_RANDOM', 'OT_HASELGROVE', 'OT_REVERSE_HALTON', 'OT_HALTON', 'OT_FAURE', 'OT_MONTE_CARLO', 'OT_FACTORIAL', 'OT_COMPOSITE', 'OT_AXIAL', 'OT_OPT_LHS', 'OT_LHS', 'OT_LHSC', 'OT_FULLFACT', 'OT_SOBOL_INDICES', 'fullfact', 'ff2n', 'pbdesign', 'bbdesign', 'ccdesign', 'lhs', 'Halton', 'LHS', 'MC', 'PoissonDisk', 'Sobol']
Total running time of the script: (0 minutes 0.658 seconds)