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Run a Benchmark
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Pareto-Set Learning
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It is a learning-based approach that aims to learn the Pareto set of a given problem.

In this notebook, we will guide you through the basic steps of using ``LibMOON``.


.. note::

   PSL in a problem with three lines of solving problem and two lines of evaluating the results.

.. code-block:: python

    from libmoon.solver.psl.core_psl import BasePSLSolver
    from libmoon.util import get_problem
    from libmoon.util.prefs import get_uniform_pref
    from torch import Tensor

    problem = get_problem(problem_name='ZDT1')
    # agg list [ ’ls ’, ’tche ’, ’mtche ’, ’pbi ’, ... ]
    prefs = get_uniform_pref(n_prob=100, n_obj=problem.n_obj, clip_eps=1e-2)
    solver = BasePSLSolver(problem, solver_name='agg_ls')
    model, _ = solver.solve()
    eval_y = problem.evaluate(model(Tensor(prefs).cuda()))