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Pareto front on Binh and Korn problem

In this example, we illustrate the use of the ParetoFront plot on the Binh and Korn multi-objective problem.

from __future__ import annotations

from gemseo import configure_logger
from gemseo.algos.doe.doe_factory import DOEFactory
from gemseo.post.post_factory import PostFactory
from gemseo.problems.analytical.binh_korn import BinhKorn

Import

The first step is to import a high-level function for logging.

configure_logger()

<RootLogger root (INFO)>

Import the optimization problem

Then, we instantiate the Binh and Korn optimization problem (see BinhKorn).

problem = BinhKorn()

Create and execute scenario

Then, we instantiate the design of experiment factory, and we request the execution of a 100-length LHS optimized by simulated annealing.

doe_factory = DOEFactory()
doe_factory.execute(problem, algo_name="OT_OPT_LHS", n_samples=100)

INFO - 13:56:13: Optimization problem:
INFO - 13:56:13:    minimize compute_binhkorn(x, y) = (4*x**2+ 4*y**2, (x-5.)**2 + (y-5.)**2)
INFO - 13:56:13:    with respect to x, y
INFO - 13:56:13:    subject to constraints:
INFO - 13:56:13:       ineq1(x, y): (x-5.)**2 + y**2 <= 25. <= 0.0
INFO - 13:56:13:       ineq2(x, y): (x-8.)**2 + (y+3)**2 >= 7.7 <= 0.0
INFO - 13:56:13:    over the design space:
INFO - 13:56:13:    +------+-------------+-------+-------------+-------+
INFO - 13:56:13:    | name | lower_bound | value | upper_bound | type  |
INFO - 13:56:13:    +------+-------------+-------+-------------+-------+
INFO - 13:56:13:    | x    |      0      |   1   |      5      | float |
INFO - 13:56:13:    | y    |      0      |   1   |      3      | float |
INFO - 13:56:13:    +------+-------------+-------+-------------+-------+
INFO - 13:56:13: Solving optimization problem with algorithm OT_OPT_LHS:
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INFO - 13:56:13: Optimization result:
INFO - 13:56:13:    Optimizer info:
INFO - 13:56:13:       Status: None
INFO - 13:56:13:       Message: None
INFO - 13:56:13:       Number of calls to the objective function by the optimizer: 100
INFO - 13:56:13:    Solution:
INFO - 13:56:13:       The solution is feasible.
INFO - 13:56:13:       Objective: 30.39964825035985
INFO - 13:56:13:       Standardized constraints:
INFO - 13:56:13:          ineq1 = [-11.77907222]
INFO - 13:56:13:          ineq2 = [-38.26307397]
INFO - 13:56:13:       Design space:
INFO - 13:56:13:       +------+-------------+-------------------+-------------+-------+
INFO - 13:56:13:       | name | lower_bound |       value       | upper_bound | type  |
INFO - 13:56:13:       +------+-------------+-------------------+-------------+-------+
INFO - 13:56:13:       | x    |      0      | 1.542975634014225 |      5      | float |
INFO - 13:56:13:       | y    |      0      | 1.269910308585573 |      3      | float |
INFO - 13:56:13:       +------+-------------+-------------------+-------------+-------+
Optimization result:
  • Design variables: [1.54297563 1.26991031]
  • Objective function: 30.39964825035985
  • Feasible solution: True


Post-process scenario

Lastly, we post-process the scenario by means of the ParetoFront plot which generates a plot or a matrix of plots if there are more than 2 objectives, plots in blue the locally non dominated points for the current two objectives, plots in green the globally (all objectives) Pareto optimal points. The plots in green denote non-feasible points. Note that the user can avoid the display of the non-feasible points.

PostFactory().execute(
    problem,
    "ParetoFront",
    show_non_feasible=False,
    objectives=["compute_binhkorn"],
    objectives_labels=["f1", "f2"],
    save=False,
    show=True,
)

PostFactory().execute(
    problem,
    "ParetoFront",
    objectives=["compute_binhkorn"],
    objectives_labels=["f1", "f2"],
    save=False,
    show=True,
)
  • Pareto front
  • Pareto front
<gemseo.post.pareto_front.ParetoFront object at 0x7f006b4a8850>

Total running time of the script: (0 minutes 0.497 seconds)

Download Python source code: plot_pareto_front_binhkorn.py

Download Jupyter notebook: plot_pareto_front_binhkorn.ipynb

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