'archipelago' doesn't parallelize to different cores

[osm3000]

Hi,

I've tried to make use of archipelago in order to parallelize the problem solution. I first tried the toy example mentioned in:
https://esa.github.io/pagmo2/docs/python/tutorials/using_archipelago.html
Indeed I can see that all the cores are utilized.
However, when I made my own problem (it reads data from a file, and learn discretization for it), I no longer see any improvement in the code:

import pygmo as pg
import numpy as np
import matplotlib.pyplot as plt
from numba import jit, float64
import pickle
class auto_quantizer:
    def __init__(self,dim, min_value, max_value, actual_data):
        self.dim = dim
        self.min_value = min_value
        self.max_value = max_value
        self.actual_data = actual_data
    def fitness(self, x):
        return [self.determine_nearest_value(x)]

    def get_bounds(self):
        return (np.full((self.dim,),self.min_value),np.full((self.dim,),self.max_value))

    def determine_nearest_value(self, x):
        total_loss = 0
        for data_point in self.actual_data:
            total_loss += np.min(np.abs(x - data_point))
        return total_loss
if __name__ == '__main__':
    actual_data = pickle.load(open('speed.pkl', 'rb'))[0]
    actual_data = np.array(actual_data)
    print ("actual_data: ", actual_data.shape)
    min_value = np.min(actual_data)
    max_value = np.max(actual_data)
    nb_levels = 8
    uni_levels = np.linspace(min_value, max_value, nb_levels)
    prob = pg.problem(auto_quantizer(nb_levels, min_value, max_value, actual_data))
    algo = pg.algorithm(pg.sea(gen = 2000))
    archi = pg.archipelago(32,algo=algo, prob=prob, pop_size=200)
    print(archi)
    archi.evolve()
    archi.wait()
    print (archi.get_champions_f())

I can see only one core is being utilized.
Any thoughts on why?