def meta_aco(ft, dimensions, bounds, dim_prec, m_stop_count, stop_prec,
m_fitness, timeout):
ants = Ant.init_colony(m_ant_count)
best_fit_per_gen = 0
grid = Grid(dimensions, bounds, dim_prec)
best_ants = []
best_ant = object
i = 0
res = {}
for ic in range(m_stop_count):
i += 1
for ant in ants:
ant.res = {}
j = 0
for dim in grid.dimensions:
j += 1
probs = []
total = 0
for r in range(dim_prec):
prob = Ant.calc_prob(dim["pheromones"][r], m_a, m_c)
probs.append(prob)
total += prob
r = rnd.random()
for rng in range(grid.prec):
prob = (probs[rng] / total if total != 0 else 0)
if r <= prob:
range_start = dim["from_bound"] + rng * dim["step"]
ant.res["d{}".format(
j)] = range_start + rnd.random() * dim["step"]
break
else:
r -= prob
ant.res["d2"] = round(ant.res["d2"])
ft_res = ft(ft_rastrigin, 2, [[-10, 10], [-10, 10]], ant.res["d2"],
stop_prec, ant.res["d1"], 0.1, 0.75, 50, fitness,
timeout)
ant.fit = m_fitness(list(ft_res.values()))
best_ant = max(ants, key=lambda p: p.fit)
pp.pprint(best_ant.res)
log = open("log.txt", mode="a+")
log.write(str(best_ant.res) + "\n")
log.close()
best_ants.append(best_ant.copy())
best_fit_per_gen = best_ant.fit
grid.weather_pheromone(m_w)
grid.leave_pheromone(ants)
Ant.clear_colony(ants)
best_ant = max(best_ants, key=lambda p: p.fit)
res = {"params": best_ant.res, "value": best_ant.fit}
return res
def main_ACO(ft, dimensions, bounds, dim_prec, stop_prec, a, c, w, ant_count,
fitness, timeout):
ants = Ant.init_colony(ant_count)
best_fit_per_gen = 0
grid = Grid(dimensions, bounds, dim_prec)
precs = [10e-2, 10e-3, 10e-4, 10e-5, 10e-6, 10e-7, 10e-8, 10e-9]
precs_len = len(precs)
curr_prec_i = 0
iters = [0] * precs_len
best_ants = []
best_ant = Ant()
best_ant.value = 100
# time_start = time.process_time()
duration = 1
# res = {}
# plt_value = [0] * (timeout + 1)
# plt_iter = [i for i in range(timeout + 1)]
best_fit = 0
best_value = 0
while (best_ant.value > -1 + precs[-1]) and (duration < timeout): #
for ant in ants:
ant.res = {}
j = 0
for dim in grid.dimensions:
j += 1
probs = []
total = 0
for r in range(dim_prec):
prob = Ant.calc_prob(dim["pheromones"][r], a, c)
probs.append(prob)
total += prob
r = rnd.random()
for rng in range(grid.prec):
prob = (probs[rng] / total if total != 0 else 0)
if r <= prob:
range_start = dim["from_bound"] + rng * dim["step"]
ant.res["d{}".format(
j)] = range_start + rnd.random() * dim["step"]
break
else:
r -= prob
ant.value = ft(list(ant.res.values()))
ant.fit = fitness(ant.value)
best_ant = max(ants, key=lambda p: p.fit)
if best_ant.fit > best_fit:
best_fit = best_ant.fit
best_value = best_ant.value
best_ants.append(best_ant.copy())
best_fit_per_gen = best_ant.fit
grid.weather_pheromone(w)
grid.leave_pheromone(ants)
Ant.clear_colony(ants)
# duration = time.process_time() - time_start
# plt_value[duration] = best_ant.value
while curr_prec_i < precs_len and best_ant.value < -1 + precs[
curr_prec_i]: #
iters[curr_prec_i] = duration
curr_prec_i += 1
if curr_prec_i == precs_len:
break
duration += 1
best_ant = max(best_ants, key=lambda p: p.fit)
best_ant.value = ft(list(best_ant.res.values()))
res = {
"params": best_ant.res,
"value": best_ant.fit,
"fvalue": best_ant.value,
"iters": iters
# "time": time.process_time() - time_start
}
# plt_value[0] = plt_value[1]
# # for value in plt_value:
# # value *= 1000
# # plt.set_ylabel("value")
# # plt.set_xlabel("iteration")
# plt.plot(plt_iter, plt_value, c="blue")
# plt.plot([0, timeout - 1], [0, 0], "--", c="red")
# plt.show()
return res
