from mealpy.evolutionary_based.GA import BaseGA
## Setting parameters
obj_func = F5
verbose = False
epoch = 500
pop_size = 50
# A - Different way to provide lower bound and upper bound. Here are some examples:
## 1. When you have different lower bound and upper bound for each parameters
lb1 = [-3, -5, 1, -10]
ub1 = [5, 10, 100, 30]
md1 = BaseGA(obj_func, lb1, ub1, verbose, epoch, pop_size)
best_pos1, best_fit1, list_loss1 = md1.train()
print(md1.solution[1])
## 2. When you have same lower bound and upper bound for each parameters, then you can use:
## + int or float: then you need to specify your problem size (number of dimensions)
problemSize = 10
lb2 = -5
ub2 = 10
md2 = BaseGA(obj_func,
lb2,
ub2,
verbose,
epoch,
pop_size,
problem_size=problemSize) # Remember the keyword "problem_size"
best_pos1, best_fit1, list_loss1 = md2.train()
#!/usr/bin/env python # ------------------------------------------------------------------------------------------------------% # Created by "Thieu Nguyen" at 10:11, 16/03/2020 % # % # Email: [email protected] % # Homepage: https://www.researchgate.net/profile/Thieu_Nguyen6 % # Github: https://github.com/thieu1995 % #-------------------------------------------------------------------------------------------------------% from opfunu.cec_basic.cec2014_nobias import * from mealpy.evolutionary_based.GA import BaseGA ## Setting parameters obj_func = F5 verbose = False epoch = 10 pop_size = 50 lb1 = [-3, -5, 1, -10] ub1 = [5, 10, 100, 30] md1 = BaseGA(obj_func, lb1, ub1, verbose, epoch, pop_size) best_pos1, best_fit1, list_loss1 = md1.train() print(md1.solution[1])
verbose = True
epoch = 100
pop_size = 50
lb1 = [-10, -5, -15, -20]
ub1 = [10, 5, 15, 20]
optimizer = BaseGA(obj_function,
lb1,
ub1,
"max",
verbose,
epoch,
pop_size,
obj_weight=[0.2, 0.5, 0.3])
best_position, best_fitness, g_best_fit_list, c_best_fit_list = optimizer.train(
)
print(best_position)
export_convergence_chart(
optimizer.history_list_g_best_fit, title='Global Best Fitness'
) # Draw global best fitness found so far in previous generations
export_convergence_chart(
optimizer.history_list_c_best_fit, title='Local Best Fitness'
) # Draw current best fitness in each previous generation
export_convergence_chart(optimizer.history_list_epoch_time,
title='Runtime chart',
y_label="Second") # Draw runtime for each generation
## On the exploration and exploitation in popular swarm-based metaheuristic algorithms
# This exploration/exploitation chart should draws for single algorithm and single fitness function
from numpy import array
## Setting parameters
problem1 = {
"obj_func": F5,
"lb": [-3, -5, 1, -10],
"ub": [5, 10, 100, 30],
"minmax": "min",
"verbose": True,
}
# A - Different way to provide lower bound and upper bound. Here are some examples:
## 1. When you have different lower bound and upper bound for each parameters
md1 = BaseGA(problem1, epoch=10, pop_size=50)
best_pos1, best_fit1 = md1.train()
print(md1.solution[1])
## 2. When you have same lower bound and upper bound for each parameters, then you can use:
## + int or float: then you need to specify your problem size (number of dimensions)
problem2 = {
"obj_func": F5,
"lb": -10,
"ub": 30,
"minmax": "min",
"verbose": True,
"problem_size": 30, # Remember the keyword "problem_size"
}
md2 = BaseGA(problem2, epoch=10,
pop_size=50) # Remember the keyword "problem_size"
md2.train()