Quick Start

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

# install LibMOO, skip it if you have already installed evox
try:
    import libmoon
except ImportError:
    import libmoon==0.1.11

Here we use the a synthetic problem as an example to show how to use LibMOON.

For more detailed list, please refer to our API documentation Supported Benchmark Datasets.

Create an algorithm and a problem

Note

Example1: Finding a size-K (K=5) Pareto solutions with four lines of code.

# import necessary modules
from libmoon.util.synthetic import synthetic_init
from libmoon.util.prefs import uniform_pref
from libmoon.util.problems import get_problem
from libmoon.solver.gradient.methods import EPOSolver

problem = get_problem(problem_name='ZDT1')
prefs = get_uniform_pref(n_prob=5, n_obj=problem.n_obj, clip_eps=1e-2)
solver = EPOSolver(step_size=1e-2, n_iter=1000, tol=1e-2, problem=problem, prefs=prefs)
res = solver.solve(x=synthetic_init(problem, prefs))