def get_best_nest(nest, newnest, fitness, n, dim, objf):
# Evaluating all new solutions
tempnest = numpy.zeros((n, dim))
tempnest = numpy.copy(nest)
bench = Benchmark()
info = bench.get_info(objf, dim)
ub = info['upper']
lb = info['lower']
optimum = info['best']
for j in range(0, n):
#for j=1:size(nest,1),
fun_fitness = bench.get_function(objf)
fnew = fun_fitness(newnest[j, :])
if fnew <= fitness[j]:
fitness[j] = fnew
tempnest[j, :] = newnest[j, :]
# Find the current best
fmin = min(fitness)
K = numpy.argmin(fitness)
bestlocal = tempnest[K, :]
return fmin, bestlocal, tempnest, fitness
def PFA(objf, n, MaxGeneration):
dim = 30
bench = Benchmark()
info = bench.get_info(objf, dim)
# dim = info['dimension']
ub = info['upper']
lb = info['lower']
optimum = info['best']
#General parameters
#n=50 #number of fireflies
# dim=10000 #dim
#lb=-50
#ub=50
#MaxGeneration=500
#FFA parameters
alpha = 0.50 # Randomness 0--1 (highly random)
betamin = 0.50 # minimum value of beta
gamma = 1 # Absorption coefficient
zn = numpy.ones(n)
zn.fill(float("inf"))
#ns(i,:)=Lb+(Ub-Lb).*rand(1,d);
ns = numpy.random.uniform(0, 1, (n, dim)) * (ub - lb) + lb
Lightn = numpy.ones(n)
Lightn.fill(float("inf"))
Lightnprev = numpy.ones(n)
Lightnprev.fill(float("inf"))
#[ns,Lightn]=init_ffa(n,d,Lb,Ub,u0)
convergence = []
s = solution()
print("PFA is optimizing F" + str(objf))
timerStart = time.time()
s.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
# Main loop
for k in range(0, MaxGeneration): # start iterations
#% This line of reducing alpha is optional
#alpha=alpha_new(alpha,MaxGeneration);
Lightnprev = Lightn
#% Evaluate new solutions (for all n fireflies)
fun_fitness = bench.get_function(objf)
for i in range(0, n):
zn[i] = fun_fitness(ns[i, :])
Lightn[i] = zn[i]
# Ranking fireflies by their light intensity/objectives
Lightn = numpy.sort(zn)
Index = numpy.argsort(zn)
ns = ns[Index, :]
#Find the current best
nso = ns
Lighto = Lightn
nbest = ns[0, :]
Lightbest = Lightn[0]
#% For output only
fbest = Lightbest
#% Move all fireflies to the better locations
# [ns]=ffa_move(n,d,ns,Lightn,nso,Lighto,nbest,...
# Lightbest,alpha,betamin,gamma,Lb,Ub);
scale = numpy.ones(dim) * abs(ub - lb)
for i in range(0, n):
# The attractiveness parameter beta=exp(-gamma*r)
for j in range(0, n):
# r=numpy.sqrt(numpy.sum((ns[i,:]-ns[j,:])**2));
# r2=numpy.sqrt(numpy.sum((ns[i,:]-ns[0,:])**2));
r = numpy.sum((ns[i, :] - ns[j, :]))
r2 = numpy.sum((ns[0, :] - ns[j, :]))
#r=1
# Update moves
if Lightn[i] > Lighto[j]: # Brighter and more attractive
# PropFA parameters
per = ((k / MaxGeneration) * 100) / 75
per2 = numpy.heaviside(per - 1, 0.5)
ratA = (numpy.absolute(Lightn[i]) - numpy.absolute(
Lightnprev[i])) / max(numpy.absolute(Lightn[i]),
numpy.absolute(Lightnprev[i]))
ratB = (numpy.absolute(Lightn[j]) - numpy.absolute(
Lightn[i])) / max(numpy.absolute(Lightn[j]),
numpy.absolute(Lightn[i]))
ratC = (numpy.absolute(fbest) - numpy.absolute(
Lightn[i])) / max(numpy.absolute(fbest),
numpy.absolute(Lightn[i]))
ratAvg = (ratA + ratB + ratC) / 3
scale2 = numpy.absolute(ub - lb)
if (Lightnprev[i] == Lightn[i]):
alpha = 10
else:
r3 = numpy.sum((ns[0, :] - ns[n - 1, :]))
alpha = (r2 / 10000) * ratAvg * numpy.exp(-k * per2)
if (Lightnprev[i] == Lightn[i]):
gamma = 1
else:
gamma = (ratB / ratC)
beta0 = 1
beta = (beta0 - betamin) * numpy.exp(
-gamma * r**2) + betamin
beta2 = (beta0 - betamin) * numpy.exp(
-gamma * r2**2) + betamin
tmpf = alpha * (numpy.random.rand(dim) - 0.5) * scale2
#ns[i,:]=ns[i,:]*(1-beta)+nso[j,:]*beta+tmpf
ns[i, :] = ns[i, :] + (beta * (nso[j, :] - ns[i, :])) + (
beta2 * (nso[0, :] - ns[i, :])) + tmpf
#ns=numpy.clip(ns, lb, ub)
IterationNumber = k
BestQuality = fbest
if (k % 1 == 0):
print([
'At iteration ' + str(k) + ' the best fitness is ' +
str(BestQuality) + ": PFA" + " :" + str(objf)
])
if (k % 1000 == 0):
convergence.append(fbest)
#
######5################# End main loop
convergence.append(Lightn[0])
convergence.append(Lightn[int(n / 2)])
convergence.append(Lightn[n - 1])
convergence.append(numpy.sum(Lightn) / n)
convergence.append(numpy.std(Lightn))
timerEnd = time.time()
s.endTime = time.strftime("%Y-%m-%d-%H-%M-%S")
s.executionTime = timerEnd - timerStart
s.convergence = convergence
s.optimizer = "PFA"
s.objfname = "F" + str(objf)
return s
archive_rate = args.archive_rate
num_children = 0
if de_alg == 'plus_de' or de_alg == 'wi_de':
num_children = int(np.floor(pop_size * args.children_size_rate))
num_children = max(num_children, 1)
subset_size = 0
if de_alg == 'sts_de':
subset_size = int(np.floor(pop_size * args.subset_size_rate))
subset_size = max(subset_size, 2)
remaining_evals = 100 * dim
fbench = Benchmark()
info = fbench.get_info(func_id, dim)
fun = fbench.get_function(func_id)
lower_bounds = np.full(dim, info['lower'])
upper_bounds = np.full(dim, info['upper'])
de = None
if de_alg == 'syn_de':
de = SynchronousDE(fun, dim, lower_bounds, upper_bounds, remaining_evals, pop_size, de_strategy, de_sf, de_cr, p_best_rate, archive_rate)
elif de_alg == 'asy_de':
de = AsynchronousDE(fun, dim, lower_bounds, upper_bounds, remaining_evals, pop_size, de_strategy, de_sf, de_cr, p_best_rate, archive_rate)
elif de_alg == 'wi_de':
de = WIDE(fun, dim, lower_bounds, upper_bounds, remaining_evals, pop_size, de_strategy, de_sf, de_cr, p_best_rate, archive_rate, num_children)
elif de_alg == 'plus_de':
de = MuPlusLambdaDE(fun, dim, lower_bounds, upper_bounds, remaining_evals, pop_size, de_strategy, de_sf, de_cr, p_best_rate, archive_rate, num_children)
elif de_alg == 'sts_de':
de = SubToSubDE(fun, dim, lower_bounds, upper_bounds, remaining_evals, pop_size, de_strategy, de_sf, de_cr, p_best_rate, archive_rate, subset_size)
else: