Grey Wolf Optimization (GWO) Sampler

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()