How to use the pyswarms.single.LocalBestPSO function in pyswarms

def optimizer_reset(self, options):
        opt = LocalBestPSO(10, 2, options)
        opt.optimize(sphere, 10)
        opt.reset()
        return opt

def optimizer_history(self, options):
        opt = LocalBestPSO(10, 2, options)
        opt.optimize(sphere, 1000)
        return opt

def test_local_wrong_kwargs(func):
    """Tests kwargs are passed the objective function for when kwargs do not exist"""

    # setup optimizer
    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}

    x_max = 10 * np.ones(2)
    x_min = -1 * x_max
    bounds = (x_min, x_max)
    opt_ps = LocalBestPSO(
        n_particles=100, dimensions=2, options=options, bounds=bounds
    )

    # run it
    with pytest.raises(TypeError) as excinfo:
        cost, pos = opt_ps.optimize(func, 1000, print_step=10, c=1, d=100)
        assert "unexpected keyword" in str(excinfo.value)

def test_local_uneeded_kwargs(func):
    """Tests kwargs are passed the objective function for when kwargs do not exist"""

    # setup optimizer
    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}

    x_max = 10 * np.ones(2)
    x_min = -1 * x_max
    bounds = (x_min, x_max)
    opt_ps = LocalBestPSO(
        n_particles=100, dimensions=2, options=options, bounds=bounds
    )

    # run it
    with pytest.raises(TypeError) as excinfo:
        cost, pos = opt_ps.optimize(func, 1000, a=1)
        assert "unexpected keyword" in str(excinfo.value)

def test_local_missed_kwargs(func):
    """Tests kwargs are passed the objective function for when kwargs do not exist"""

    # setup optimizer
    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}

    x_max = 10 * np.ones(2)
    x_min = -1 * x_max
    bounds = (x_min, x_max)
    opt_ps = LocalBestPSO(
        n_particles=100, dimensions=2, options=options, bounds=bounds
    )

    # run it
    with pytest.raises(TypeError) as excinfo:
        cost, pos = opt_ps.optimize(func, 1000, a=1)
        assert "missing 1 required positional argument" in str(excinfo.value)

def test_local_no_kwargs(func):
    """Tests if no kwargs/args are passed properly to the objective function for when kwargs are present"""

    # setup optimizer
    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}

    x_max = 10 * np.ones(2)
    x_min = -1 * x_max
    bounds = (x_min, x_max)
    opt_ps = LocalBestPSO(
        n_particles=100, dimensions=2, options=options, bounds=bounds
    )

    # run it
    cost, pos = opt_ps.optimize(func, iters=1000)

    assert np.isclose(cost, 0, rtol=1e-03)
    assert np.isclose(pos[0], 1.0, rtol=1e-03)
    assert np.isclose(pos[1], 1.0, rtol=1e-03)

def test_local_correct_pos(self, options):
        """ Test to check local optimiser returns the correct position corresponding to the best cost """
        opt = LocalBestPSO(n_particles=10, dimensions=2, options=options)
        cost, pos = opt.optimize(sphere, iters=5)
        # find best pos from history
        min_cost_idx = np.argmin(opt.cost_history)
        min_pos_idx = np.argmin(sphere(opt.pos_history[min_cost_idx]))
        assert np.array_equal(opt.pos_history[min_cost_idx][min_pos_idx], pos)

def test_local_kwargs(func):
    """Tests if kwargs are passed properly to the objective function for when kwargs are present"""

    # setup optimizer
    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}

    x_max = 10 * np.ones(2)
    x_min = -1 * x_max
    bounds = (x_min, x_max)
    opt_ps = LocalBestPSO(
        n_particles=100, dimensions=2, options=options, bounds=bounds
    )

    # run it
    cost, pos = opt_ps.optimize(func, 1000, a=1, b=100)

    assert np.isclose(cost, 0, rtol=1e-03)
    assert np.isclose(pos[0], 1.0, rtol=1e-03)
    assert np.isclose(pos[1], 1.0, rtol=1e-03)

#Benchmark PSO Global Best

import numpy as np
import pyswarms as ps
from pyswarms.utils.functions import single_obj as fx

options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k':2, 'p':2}

optimizer = ps.single.LocalBestPSO(n_particles=10, dimensions=2, options=options)

cost, pos = optimizer.optimize(fx.sphere_func, print_step=100, iters=1000, verbose=3)