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修改启发式算法和遗传算法实现

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hit_lu
2023-07-20 20:02:43 +08:00
parent 6e56f796f0
commit 315b747b19
10 changed files with 368 additions and 461 deletions
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57
base_optimizer/optimizer_scanbased.py
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@ -3,11 +3,11 @@ from base_optimizer.optimizer_common import *
@timer_wrapper
def optimizer_scanbased(component_data, pcb_data, hinter):
def optimizer_genetic_scanning(component_data, pcb_data, hinter):
population_size = 200 # 种群规模
crossover_rate, mutation_rate = .4, .02
n_generation = 5
n_generation = 500
component_points = [0] * len(component_data)
for i in range(len(pcb_data)):
@ -31,49 +31,51 @@ def optimizer_scanbased(component_data, pcb_data, hinter):
pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
# todo: 过程写的有问题，暂时不想改
sigma_scaling(pop_val, 1)
with tqdm(total=n_generation) as pbar:
pbar.set_description('hybrid genetic process')
new_pop_val, new_pop_individual = [], []
# min-max convert
max_val = 1.5 * max(pop_val)
convert_pop_val = list(map(lambda v: max_val - v, pop_val))
for _ in range(n_generation):
# 交叉
for pop in range(population_size):
if pop % 2 == 0 and np.random.random() < crossover_rate:
index1, index2 = roulette_wheel_selection(pop_val), -1
index1, index2 = roulette_wheel_selection(convert_pop_val), -1
while True:
index2 = roulette_wheel_selection(pop_val)
index2 = roulette_wheel_selection(convert_pop_val)
if index1 != index2:
break
# 两点交叉算子
offspring1, offspring2 = cycle_crossover(pop_individual[index1], pop_individual[index2])
# 变异
if np.random.random() < mutation_rate:
offspring1 = swap_mutation(offspring1)
if np.random.random() < mutation_rate:
offspring2 = swap_mutation(offspring2)
_, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, offspring1)
pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
pop_individual.append(offspring1)
new_pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
new_pop_individual.append(offspring1)
_, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, offspring2)
pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
pop_individual.append(offspring2)
new_pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
new_pop_individual.append(offspring2)
sigma_scaling(pop_val, 1)
# generate next generation
top_k_index = get_top_k_value(pop_val, population_size - len(new_pop_individual), reverse=False)
for index in top_k_index:
new_pop_individual.append(pop_individual[index])
new_pop_val.append(pop_val[index])
# 变异
if np.random.random() < mutation_rate:
index_ = roulette_wheel_selection(pop_val)
offspring = swap_mutation(pop_individual[index_])
_, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, offspring)
pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
pop_individual.append(offspring)
sigma_scaling(pop_val, 1)
new_population, new_popval = [], []
for index in get_top_k_value(pop_val, population_size):
new_population.append(pop_individual[index])
new_popval.append(pop_val[index])
pop_individual, pop_val = new_population, new_popval
pop_individual, pop_val = new_pop_individual, new_pop_val
sigma_scaling(pop_val, 1)
# select the best individual
pop = np.argmin(pop_val)
@ -98,7 +100,6 @@ def convert_individual_2_result(component_points, pop):
feeder_part[gene], feeder_base_points[gene] = idx, component_points[idx]
# TODO: 暂时未考虑可用吸嘴数的限制
# for _ in range(math.ceil(sum(component_points) / max_head_index)):
while True:
# === 周期内循环 ===
assigned_part = [-1 for _ in range(max_head_index)] # 当前扫描到的头分配元件信息