Class or Function Names
- GreyWolfOptimizationSampler
Example
from __future__ import annotations
import matplotlib.pyplot as plt
import optuna
import optunahub
GreyWolfOptimizationSampler = optunahub.load_module(
"samplers/grey_wolf_optimization"
).GreyWolfOptimizationSampler
if __name__ == "__main__":
def objective(trial: optuna.trial.Trial) -> float:
x = trial.suggest_float("x", -10, 10)
y = trial.suggest_float("y", -10, 10)
return x**2 + y**2
# Note: `n_trials` should match the `n_trials` passed to `study.optimize`.
sampler = GreyWolfOptimizationSampler(n_trials=100)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=sampler.n_trials)
optuna.visualization.matplotlib.plot_optimization_history(study)
plt.show()
Others
Reference
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
- Package
- samplers/grey_wolf_optimization
- Author
- k-onoue
- License
- MIT License
- Verified Optuna version
- 3.6.1
- Last update
- 2024-09-27