def test_gclde_parallel_eval(problem):
# parallel function evaluation using GCL_DE
t0 = time.perf_counter()
evals = 0
for i in range(100000):
ret = gcldecpp.minimize(problem.fun,
problem.bounds,
popsize=256,
max_evaluations=200000,
workers=mp.cpu_count())
evals += ret.nfev
print("{0}: time = {1:.1f} fun = {2:.3f} nfev = {3}".format(
i + 1, dtime(t0), ret.fun, evals))
return ret
def minimize(self,
fun,
bounds,
guess=None,
sdevs=None,
rg=Generator(MT19937()),
store=None,
workers=None):
ret = gcldecpp.minimize(
fun,
bounds,
popsize=self.popsize,
max_evaluations=self.max_eval_num(store),
stop_fitness=self.stop_fitness,
pbest=self.pbest,
f0=self.f0,
cr0=self.cr0,
rg=rg,
runid=self.get_count_runs(store),
workers=self.workers if workers is None else workers)
return ret.x, ret.fun, ret.nfev
def test_rosen_gclde_parallel():
popsize = 8
dim = 2
testfun = Rosen(dim)
max_eval = 10000
limit = 0.00001
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
ret = gcldecpp.minimize(wrapper.eval,
dim,
testfun.bounds,
max_evaluations=max_eval,
popsize=popsize,
workers=mp.cpu_count())
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + popsize >= ret.nfev) # too much function calls
assert (max_eval // popsize + 2 > ret.nit) # too much iterations
assert (ret.nfev == wrapper.get_count()
) # wrong number of function calls returned
assert (almost_equal(ret.x, wrapper.get_best_x())) # wrong best X returned
assert (ret.fun == wrapper.get_best_y()) # wrong best y returned