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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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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)