Run a Benchmark

Pareto-Set Learning

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.

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()))