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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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8
base_optimizer/optimizer_common.py
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@ -49,6 +49,12 @@ feeder_width = {'SM8': (7.25, 7.25), 'SM12': (7.00, 20.00), 'SM16': (7.00, 22.00
# 可用吸嘴数量限制
nozzle_limit = {'CN065': 6, 'CN040': 6, 'CN220': 6, 'CN400': 6, 'CN140': 6}
# 时间参数
t_cycle = 0.3
t_pick, t_place = .078, .051 # 贴装/拾取用时
t_nozzle_put, t_nozzle_pick = 0.9, 0.75 # 装卸吸嘴用时
t_nozzle_change = t_nozzle_put + t_nozzle_pick
t_fix_camera_check = 0.12 # 固定相机检测时间
def axis_moving_time(distance, axis=0):
distance = abs(distance) * 1e-3
@ -880,7 +886,7 @@ def constraint_swap_mutation(component_points, individual):
offspring = individual.copy()
idx, component_index = 0, random.randint(0, len(component_points) - 1)
for points in component_points.values():
for _, points in component_points:
if component_index == 0:
while True:
index1, index2 = random.sample(range(points + max_machine_index - 2), 2)

2
base_optimizer/optimizer_feederpriority.py
View File

@ -2,7 +2,7 @@ from base_optimizer.optimizer_common import *
@timer_wrapper
def feeder_allocate(component_data, pcb_data, feeder_data, nozzle_pattern, figure=False):
def feeder_allocate(component_data, pcb_data, feeder_data, figure=False):
feeder_points, feeder_division_points = defaultdict(int), defaultdict(int) # 供料器贴装点数
mount_center_pos = defaultdict(int)

59
base_optimizer/optimizer_hybridgenetic.py
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@ -234,8 +234,8 @@ def cal_individual_val(component_nozzle, component_point_pos, designated_nozzle,
return V[-1], pickup_result, pickup_cycle_result
def convert_individual_2_result(component_data, component_point_pos, designated_nozzle, pickup_group, pickup_group_cycle,
pair_group, feeder_lane, individual):
def convert_individual_2_result(component_data, component_point_pos, designated_nozzle, pickup_group,
pickup_group_cycle, pair_group, feeder_lane, individual):
component_result, cycle_result, feeder_slot_result = [], [], []
placement_result, head_sequence_result = [], []
@ -418,19 +418,19 @@ def optimizer_hybrid_genetic(pcb_data, component_data, hinter=True):
pick_part = pickup[pickup_index]
# 检查槽位占用情况
if feeder_lane[slot] is not None and pick_part is not None:
if feeder_lane[slot] and pick_part:
assign_available = False
break
# 检查机械限位冲突
if pick_part is not None and (slot - CT_Head[pick_part][0] * interval_ratio <= 0 or
slot + (max_head_index - CT_Head[pick_part][1] - 1) * interval_ratio > max_slot_index // 2):
if pick_part and (slot - CT_Head[pick_part][0] * interval_ratio <= 0 or slot + (
max_head_index - CT_Head[pick_part][1] - 1) * interval_ratio > max_slot_index // 2):
assign_available = False
break
if assign_available:
for idx, component in enumerate(pickup):
if component is not None:
if component:
feeder_lane[assign_slot + idx * interval_ratio] = component
CT_Group_slot[CTIdx] = assign_slot
break
@ -509,32 +509,31 @@ def optimizer_hybrid_genetic(pcb_data, component_data, hinter=True):
with tqdm(total=n_generations) as pbar:
pbar.set_description('hybrid genetic process')
# calculate fitness value
pop_val = []
for pop_idx, individual in enumerate(population):
val, _, _ = cal_individual_val(component_nozzle, component_point_pos, designated_nozzle, pickup_group,
pickup_group_cycle, pair_group, feeder_part_arrange, individual)
pop_val.append(val) # val is related to assembly time
for _ in range(n_generations):
# calculate fitness value
pop_val = []
for pop_idx, individual in enumerate(population):
val, _, _ = cal_individual_val(component_nozzle, component_point_pos, designated_nozzle, pickup_group,
pickup_group_cycle, pair_group, feeder_part_arrange, individual)
pop_val.append(val)
idx = np.argmin(pop_val)
if len(best_pop_val) == 0 or pop_val[idx] < best_pop_val[-1]:
best_individual = copy.deepcopy(population[idx])
best_pop_val.append(pop_val[idx])
# idx = np.argmin(pop_val)
# if len(best_pop_val) == 0 or pop_val[idx] < best_pop_val[-1]:
# best_individual = copy.deepcopy(population[idx])
# best_pop_val.append(pop_val[idx])
# min-max convert
max_val = 1.5 * max(pop_val)
pop_val = list(map(lambda v: max_val - v, pop_val))
convert_pop_val = list(map(lambda v: max_val - v, pop_val))
# crossover and mutation
c = 0
new_population = []
new_population, new_pop_val = [], []
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
@ -552,13 +551,27 @@ def optimizer_hybrid_genetic(pcb_data, component_data, hinter=True):
new_population.append(offspring1)
new_population.append(offspring2)
# selection
top_k_index = get_top_k_value(pop_val, population_size - len(new_population))
val, _, _ = cal_individual_val(component_nozzle, component_point_pos, designated_nozzle,
pickup_group,
pickup_group_cycle, pair_group, feeder_part_arrange, offspring1)
new_pop_val.append(val)
val, _, _ = cal_individual_val(component_nozzle, component_point_pos, designated_nozzle,
pickup_group,
pickup_group_cycle, pair_group, feeder_part_arrange, offspring2)
new_pop_val.append(val)
# generate next generation
top_k_index = get_top_k_value(pop_val, population_size - len(new_population), reverse=False)
for index in top_k_index:
new_population.append(population[index])
new_pop_val.append(pop_val[index])
population = new_population
pop_val = new_pop_val
pbar.update(1)
best_individual = population[np.argmin(pop_val)]
return convert_individual_2_result(component_data, component_point_pos, designated_nozzle, pickup_group,
pickup_group_cycle, pair_group, feeder_lane, best_individual)

57
base_optimizer/optimizer_scanbased.py
View File

@ -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