def generate_point(nobjs, population_size):
# define the problem definition
problem = DTLZ2(nobjs=nobjs)
# instantiate the optimization algorithm
algorithm = NSGAII(problem, population_size=population_size)
algorithm.run(1000)
# mutate_point = RandomGenerator().generate(problem=problem)
return algorithm.result[0].objectives._data
pass
def draw_pareto():
# define the problem definition
problem = DTLZ2(nobjs=2)
# instantiate the optimization algorithm
algorithm = NSGAII(problem, population_size=20)
# optimize the problem using 10,000 function evaluations
algorithm.run(10000)
# plot the results using matplotlib
plt.figure(figsize=(6, 6), dpi=80)
plt.figure()
plt.subplot(111)
plt.scatter([s.objectives[1] for s in algorithm.result],
[s.objectives[0] for s in algorithm.result],
s=36,
color='blue')
plt.xlim([0, 1.1])
plt.ylim([0, 1.1])
plt.xlabel("$f_1(x)$")
plt.ylabel("$f_2(x)$")
plt.show()
import random
from platypus import (NSGAII, DTLZ2, Solution, EpsilonBoxArchive, GenerationalDistance, InvertedGenerationalDistance,
Hypervolume, EpsilonIndicator, Spacing)
from platypus import *
# create the problem
problem = DTLZ2(3)
# solve it using NSGA-II
algorithm = NSGAII(problem)
algorithm.run(10000)
# generate the reference set for 3D DTLZ2
reference_set = EpsilonBoxArchive([0.02, 0.02, 0.02])
for _ in range(1000):
solution = Solution(problem)
solution.variables = [random.uniform(0,1) if i < problem.nobjs-1 else 0.5 for i in range(problem.nvars)]
solution.evaluate()
reference_set.add(solution)
# compute the indicators
gd = GenerationalDistance(reference_set)
print("Generational Distance:", gd.calculate(algorithm.result))
igd = InvertedGenerationalDistance(reference_set)
print("Inverted Generational Distance:", igd.calculate(algorithm.result))
hyp = Hypervolume(reference_set)
def main():
# define refer point
reference_point = [1.5, 1.5]
# define the problem definition
problem = DTLZ2(nobjs=2)
# instantiate the optimization algorithm
algorithm = NSGAII(problem, population_size=5)
point_list = write_pareto(problem, algorithm)
i = 1
k = 1
plt.rcParams.update({'figure.max_open_warning': 0})
while i <= 3125: # 1 5 25 625 3125 15625
if math.log(i, 5).is_integer():
flag = 1
else:
flag = 0
if flag:
plt.figure(figsize=(6, 18), dpi=80)
fig = plt.figure(k)
k = k + 1
# i-311
plt.subplot(311)
plt.title('dtlz2-' + str(i) + '-1', fontsize=14)
draw_point(plt, reference_point)
# drawsphere(plt)
draw_point_list(plt, point_list=point_list)
if flag:
# i - 312
plt.subplot(312)
plt.title('dtlz2-' + str(i) + '-2', fontsize=14)
# drawsphere(plt)
draw_point_list(plt, point_list)
draw_point(plt, reference_point)
# mutation point
mutate_point = generate_point(nobjs=2, population_size=1)
if flag:
plt.scatter(mutate_point[0],
mutate_point[1],
s=60,
c='black',
marker='+')
# pop + 1 hyper volume compute
point_collection, hyp_value = hyp_compute(
point_list, mutate_point, reference_point=reference_point)
# pop + 1 hyper volume compute
# loop until hyper nearly not change
point_list, hyper_max = list_max(point_list, hyp_value, mutate_point)
# i - 313
if flag:
plt.subplot(313)
plt.title('dtlz2-' + str(i) + '-3', fontsize=14)
# drawsphere(plt)
draw_point_list(plt, point_list)
draw_point(plt, reference_point)
# print hyp_value
print('Current order is %d, and the hyper_max is %10f' %
(i, hyper_max))
i = i + 1
# pop mutation
fig.tight_layout()
# loop until hyper nearly not change
# change each children graph distance
fig.tight_layout()
plt.show()
pass
from platypus import NSGAII, NSGAIII, DTLZ2, Hypervolume, experiment, calculate, display
if __name__ == "__main__":
algorithms = [NSGAII, (NSGAIII, {"divisions_outer": 12})]
problems = [DTLZ2(3)]
# run the experiment
results = experiment(algorithms, problems, nfe=10000, seeds=10)
# calculate the hypervolume indicator
hyp = Hypervolume(minimum=[0, 0, 0], maximum=[1, 1, 1])
hyp_result = calculate(results, hyp)
display(hyp_result, ndigits=3)