run_BCM.py

#!/usr/bin/env python
# Created by "Thieu" at 08:55, 21/02/2022 ----------%                                                                               
#       Email: nguyenthieu2102@gmail.com            %                                                    
#       Github: https://github.com/thieu1995        %                         
# --------------------------------------------------%

from models.news.BCM import BaseBCM
from numpy import sum, pi, exp, sqrt, cos


## You can create whatever function you want here
def func_sum(solution):
    return sum(solution ** 2)


def func_ackley(solution):
    a, b, c = 20, 0.2, 2 * pi
    d = len(solution)
    sum_1 = -a * exp(-b * sqrt(sum(solution ** 2) / d))
    sum_2 = exp(sum(cos(c * solution)) / d)
    return sum_1 - sum_2 + a + exp(1)


## You can create different bound for each dimension like this
# lb = [-15, -10, -3, -15, -10, -3, -15, -10, -3, -15, -10, -3, -15, -10, -3, -100, -40, -50]
# ub = [15, 10, 3, 15, 10, 3, 15, 10, 3, 15, 10, 3, 15, 10, 3, 20, 200, 1000]
## if you choose this way, the problem_size need to be same length as lb and ub

## Or bound is the same for all dimension like this
problem_size = 10
lb = [-10] * problem_size
ub = [10] * problem_size
## if you choose this way, the problem_size can be anything you want


## Setting parameters
obj_func = func_sum  # Objective function (Your minimum or maximum function)
verbose = True
epoch = 1000
pop_size = 50

md1 = BaseBCM(obj_func, lb, ub, verbose, epoch, pop_size)
best_pos1, best_fit1, list_loss1 = md1.train()
# return : the global best solution, the fitness of global best solution and the loss of training process in each epoch/iteration
print(md1.solution[0])
print(md1.solution[1])
print(md1.loss_train)