def SMSEMOA(N, maxgen, problem, encoding):
D = problem.D
M = problem.M
lower = problem.lower
upper = problem.upper
pc = 1
pm = 1 / D
pop = initial(N, D, M, lower, upper, problem, encoding)
gen = 1
start = time.time()
fig = plt.figure()
while gen <= maxgen:
for i in range(N):
k = np.random.randint(0, len(pop))
l = np.random.randint(0, len(pop))
# keep different
while k == l:
l = np.random.randint(0, len(pop))
pop_parent = [pop[k], pop[l]]
pop_offspring = crossMutation(pop_parent, D, lower, upper, pc, pm,
problem)
if checkDominance(pop_offspring[0], pop_offspring[1]):
q = pop_offspring[0]
else:
q = pop_offspring[1]
pop_combine = np.hstack([pop, q])
pop_combine = list(pop_combine)
pop = Reduce(pop_combine, M)
gen += 1
start1 = time.time()
draw(problem, pop, M, fig)
if gen < maxgen:
plt.clf()
end1 = time.time()
print("3：%7fs" % (end1 - start1))
if (gen % 10) == 0:
print("%d gen has completed!\n" % gen)
plt.ioff()
end = time.time()
print("runtime：%2fs" % (end - start))
#plt.show()
# PRINT INDICATOR
score = HV(pop)
print("HV indicator:%f" % score)
plt.show()
def IBEA(N, maxgen, problem, encoding):
start = time.time()
D = problem.D
M = problem.M
lower = problem.lower
upper = problem.upper
pc = 1
pm = 1 / D
kappa = 0.05
pop = initial(N, D, M, lower, upper, problem, encoding)
gen = 1
plt.ion()
fig = plt.figure()
while gen <= maxgen:
Fitness, fitness, pop = Calfitness(pop, kappa, len(pop))
pop_parent1 = TournamentSelection(pop, N)
pop_parent2 = TournamentSelection(pop, N)
pop_parent = pop_parent1 + pop_parent2
length = len(pop_parent)
pop_offspring = crossMutation(pop_parent, D, lower, upper, pc, pm,
problem, 1, 2)
pop_mixed = pop + pop_offspring
Fitness, fitness, pop = Calfitness(pop_mixed, kappa, len(pop_mixed))
pop = EnviromentSelection(pop_mixed, fitness, length)
draw(problem, pop, M, fig)
if gen < maxgen:
plt.clf()
if gen % 10 == 0:
print("%d gen has completed" % gen)
gen += 1
end = time.time()
plt.ioff()
print("runtime: %.2fs" % (end - start))
# PRINT INDICATOR
temp = []
for i in range(N):
temp.append(pop[i].obj)
popObj = np.vstack(temp)
score = HV(popObj)
print("HV indicator:%f" % score)
plt.show()
def NSGAII(N, maxgen, problem, encoding):
D = problem.D
M = problem.M
lower = problem.lower
upper = problem.upper
pc = 0.9
pm = 1 / D
pop = initial(N, D, M, lower, upper, problem, encoding)
F1, pop_non, _ = nonDominatedSort(N, pop)
pop = crowdingDistance(F1, pop_non, M)
start = time.time()
gen = 1
fig = plt.figure()
plt.ion()
while gen <= maxgen:
pop_parent1 = tournamentSelection(pop, N)
pop_parent2 = tournamentSelection(pop, N)
pop_parent = pop_parent1 + pop_parent2
#print(np.size(pop_parent))
pop_offspring = crossMutation(pop_parent, D, lower, upper, pc, pm,
problem)
pop_combine = pop + pop_offspring
F, pop_combine_non, _ = nonDominatedSort(len(pop_combine), pop_combine)
pop_combine2 = crowdingDistance(F, pop_combine_non, M)
pop = elitism(N, pop_combine2)
draw(problem, pop, M, fig)
if gen < maxgen:
plt.clf()
if (gen % 10) == 0:
print("%d gen has completed!\n" % gen)
gen = gen + 1
end = time.time()
plt.ioff()
print("runtime：%2fs" % (end - start))
#PRINT INDICATOR
score = HV(pop)
print("HV indicator:%f" % score)
plt.show()
def SPEA2(N, maxgen, problem, encoding):
start = time.time()
D = problem.D
M = problem.M
lower = problem.lower
upper = problem.upper
pc = 1
pm = 1 / D
k = round(np.sqrt(2 * N))
pop = initial(N, D, M, lower, upper, problem, encoding)
pop_ex = []
fig = plt.figure()
plt.ion()
gen = 1
while True:
pop_combine = pop + pop_ex
pop_combine = Calfitness(pop_combine, k, M)
pop_ex = EnvironmentalSelection(pop_combine, N, M)
draw(problem, pop_ex, M, fig)
if gen < maxgen:
plt.clf()
if gen > maxgen:
break
pop_mating = MatingSelection(pop_ex, N)
#print(len(pop_mating))
pop = crossMutation(pop_mating, D, lower, upper, pc, pm, problem)
#print("!!%d"%len(pop))
gen += 1
if gen % 10 == 0:
print("%d gen has completed" % gen)
end = time.time()
plt.ioff()
print("runtime: %.2fs" % (end - start))
plt.show()
def MOEAD(N,maxgen,problem,encoding,type = 1):
start = time.time()
D = problem.D
M = problem.M
lower = problem.lower
upper = problem.upper
pc = 1
pm = 1 / D
W, N = UniformPoint(N, M)
T = np.ceil(N/10)
T = int(T)
B = FindNeighbour(W, N, M, T)
#print(W[99])
pop = initial(N, D, M, lower, upper, problem, encoding)
z = Best(pop)
gen = 1
plt.ion()
fig = plt.figure()
while gen<=maxgen:
for i in range(N):
k = np.random.randint(0,T)
l = np.random.randint(0,T)
while k==l:
l = np.random.randint(0,T)
pop_parent = [pop[B[i][k]],pop[B[i][l]]]
pop_offspring = crossMutation(pop_parent, D, lower, upper, pc, pm, problem)
if checkDominance(pop_offspring[0],pop_offspring[1]):
y = pop_offspring[0]
else:
y = pop_offspring[1]
for j in range(len(z)):
if y.obj[j]<z[j]:
z[j] = y.obj[j]
#小心这使用的权重，之前搞错了
for j in range(T):
if Tchebycheff(y,W[B[i][j]],z) < Tchebycheff(pop[B[i][j]],W[B[i][j]],z):
pop[B[i][j]] = y
draw(problem,pop, M, fig)
# print(Tchebycheff(pop[99],W[99],z))
# print(pop[99].obj[0],pop[99].obj[1])
if gen < maxgen:
plt.clf()
if (gen % 10) == 0:
print("%d gen has completed!\n" % gen)
gen = gen + 1;
end = time.time()
plt.ioff()
print("runtime：%2fs" % (end - start))
# for i in range(N):
# print("population %f obj[0]: %f obj[1]: %f obj[2]: %f"%(i,pop[i].obj[0],pop[i].obj[1],pop[i].obj[2]))
# print("x[0]:%f x[1]:%f x[2]:%f x[3]:%f x[4]:%f x[5]:%f x[6]:%f x[7]:%f x[8]:%f"
# %(pop[i].x[0],pop[i].x[1],pop[i].x[2],pop[i].x[3],pop[i].x[4],pop[i].x[5],pop[i].x[6],pop[i].x[7],pop[i].x[8])
# )
# print("here")
# PRINT INDICATOR
score = HV(pop)
print("HV indicator:%f" % score)
plt.show()