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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import pytest
import numpy as np
from gradient_free_optimizers import ParticleSwarmOptimizer
from ._base_para_test import _base_para_test_func
def objective_function(para):
score = -para["x1"] * para["x1"]
return score
search_space = {"x1": np.arange(-100, 101, 1)}
parallel_tempering_para = [
({"inertia": 0.5}),
({"inertia": 0.9}),
({"inertia": 0.1}),
({"inertia": 0}),
({"inertia": 2}),
({"cognitive_weight": 0.5}),
({"cognitive_weight": 0.9}),
({"cognitive_weight": 0.1}),
({"cognitive_weight": 0}),
({"cognitive_weight": 2}),
({"social_weight": 0.5}),
({"social_weight": 0.9}),
({"social_weight": 0.1}),
({"social_weight": 0}),
({"social_weight": 2}),
({"temp_weight": 0.5}),
({"temp_weight": 0.9}),
({"temp_weight": 0.1}),
({"temp_weight": 0}),
({"temp_weight": 2}),
({"rand_rest_p": 0}),
({"rand_rest_p": 0.5}),
({"rand_rest_p": 1}),
]
pytest_wrapper = ("opt_para", parallel_tempering_para)
@pytest.mark.parametrize(*pytest_wrapper)
def test_hill_climbing_para(opt_para):
_base_para_test_func(opt_para, ParticleSwarmOptimizer)
SimonBlanke /
Gradient-Free-Optimizers
