def do(self, problem, algorithm):
import matplotlib.pyplot as plt
if problem.n_var != 2 or problem.n_obj != 1:
raise Exception(
"This visualization can only be used for problems with two variables and one objective!")
# draw the problem surface
FitnessLandscape(problem,
_type="contour",
kwargs_contour=dict(alpha=0.3),
n_samples=self.n_samples_for_surface,
close_on_destroy=False).do()
# get the population
off = algorithm.pop
pop = algorithm.pop if self.last_pop is None else self.last_pop
pbest = Population.create(*off.get("pbest"))
for i in range(len(pop)):
plt.plot([off[i].X[0], pop[i].X[0]], [off[i].X[1], pop[i].X[1]], color="blue", alpha=0.5)
plt.plot([pbest[i].X[0], pop[i].X[0]], [pbest[i].X[1], pop[i].X[1]], color="red", alpha=0.5)
plt.plot([pbest[i].X[0], off[i].X[0]], [pbest[i].X[1], off[i].X[1]], color="red", alpha=0.5)
X, F, CV = pbest.get("X", "F", "CV")
plt.scatter(X[:, 0], X[:, 1], edgecolors="red", marker="*", s=70, facecolors='none', label="pbest")
X, F, CV = off.get("X", "F", "CV")
plt.scatter(X[:, 0], X[:, 1], color="blue", marker="o", s=30, label="particle")
X, F, CV = pop.get("X", "F", "CV")
plt.scatter(X[:, 0], X[:, 1], color="blue", marker="o", s=30, alpha=0.5)
opt = algorithm.opt
X, F, CV = opt.get("X", "F", "CV")
plt.scatter(X[:, 0], X[:, 1], color="black", marker="x", s=100, label="gbest")
xl, xu = problem.bounds()
plt.xlim(xl[0], xu[0])
plt.ylim(xl[1], xu[1])
plt.title(f"Generation: %s \nf: %.5E" % (algorithm.n_gen, opt[0].F[0]))
plt.legend()
self.last_pop = off.copy(deep=True)
def do(self, problem, algorithm):
# check whether the visualization can be done or not - throw exception or simply do nothing
if problem.n_var != 2 or problem.n_obj != 1:
raise Exception("This visualization can only be used for problems with two variables and one objective!")
# draw the problem surface
FitnessLandscape(problem, _type="contour", kwargs_contour=dict(alpha=0.5)).do()
# get the population
pop = algorithm.pop
X, F, CV = pop.get("X", "F", "CV")
plt.scatter(X[:, 0], X[:, 1], color="blue", marker="o", s=70)
is_new = np.full(len(pop), True)
if self.last_pop is not None:
for k, ind in enumerate(pop):
if ind in self.last_pop:
is_new[k] = False
# plot the new population
if is_new.sum() > 0:
X, F, CV = pop[is_new].get("X", "F", "CV")
plt.scatter(X[:, 0], X[:, 1], color="red", marker="*", s=70)
if hasattr(algorithm, "off") and algorithm.off is not None:
X, F, CV = algorithm.off.get("X", "F", "CV")
plt.scatter(X[:, 0], X[:, 1], color="purple", marker="*", s=40)
xl, xu = problem.bounds()
plt.xlim(xl[0], xu[0])
plt.ylim(xl[1], xu[1])
plt.title(f"Generation: {algorithm.n_gen}")
plt.legend()
# store the current population as the last
self.last_pop = set(pop)
def do(self, problem, algorithm):
# check whether the visualization can be done or not - throw exception or simply do nothing
if problem.n_var != 2 or problem.n_obj != 1:
raise Exception(
"This visualization can only be used for problems with two variables and one objective!"
)
# draw the problem surface
# if algorithm.surrogate.targets["F"].doe is not None:
# problem = algorithm.surrogate
plot = FitnessLandscape(problem,
_type="contour",
kwargs_contour=dict(alpha=0.5))
plot.do()
# get the population
pop = algorithm.pop
X, F, CV = pop.get("X", "F", "CV")
plt.scatter(X[:, 0],
X[:, 1],
facecolor="none",
edgecolors="black",
marker="o",
s=50,
label="Solutions")
if hasattr(algorithm, "off") and algorithm.off is not None:
X, F, CV = algorithm.off.get("X", "F", "CV")
plt.scatter(X[:, 0],
X[:, 1],
color="green",
marker="D",
s=30,
label="Offsprings")
is_new = np.full(len(pop), True)
if self.last_pop is not None:
for k, ind in enumerate(pop):
if ind in self.last_pop:
is_new[k] = False
# plot the new population
if is_new.sum() > 0:
X, F, CV = pop[is_new].get("X", "F", "CV")
plt.scatter(X[:, 0],
X[:, 1],
color="red",
marker="*",
s=70,
label="Survivors")
xl, xu = problem.bounds()
plt.xlim(xl[0], xu[0])
plt.ylim(xl[1], xu[1])
plt.title(f"Generation: {algorithm.n_gen}")
plt.legend()
# store the current population as the last
self.last_pop = set(pop)
plt.show()
return plt.gcf()
algorithm,
termination=('n_gen', 10000),
seed=1,
save_history=True,
verbose=False)
print(ret.F)
with Video(File("mm.mp4")) as vid:
for algorithm in ret.history:
if algorithm.n_gen % 100 == 0:
fl = FitnessLandscape(problem,
_type="contour",
kwargs_contour=dict(linestyles="solid",
offset=-1,
alpha=0.4))
fl.do()
for k, solver in enumerate(algorithm.solvers):
X = solver.pop.get("X")
plt.scatter(X[:, 0], X[:, 1], marker="x", label=k)
for k, solver in enumerate(algorithm.niches):
X = solver.opt.get("X")
plt.scatter(X[:, 0],
X[:, 1],
marker="x",
color="black",
label="Niche %s" % k,