167 lines
7.4 KiB
Python
167 lines
7.4 KiB
Python
import itertools
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from base_optimizer.optimizer_common import *
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@timer_wrapper
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def optimizer_scanbased(component_data, pcb_data, hinter):
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population_size = 200 # 种群规模
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crossover_rate, mutation_rate = .4, .02
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n_generation = 5
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component_points = [0] * len(component_data)
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for i in range(len(pcb_data)):
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part = pcb_data.loc[i]['part']
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part_index = component_data[component_data['part'] == part].index.tolist()[0]
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component_points[part_index] += 1
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nozzle_type = component_data.loc[part_index]['nz']
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if nozzle_type not in nozzle_limit.keys() or nozzle_limit[nozzle_type] <= 0:
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info = 'there is no available nozzle [' + nozzle_type + '] for the assembly process'
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raise ValueError(info)
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# randomly generate permutations
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generation_ = np.array([i for i in range(max_slot_index // 2)]) # 仅考虑前基座
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pop_individual, pop_val = [], []
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for _ in range(population_size):
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np.random.shuffle(generation_)
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pop_individual.append(generation_.tolist())
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_, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, pop_individual[-1])
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pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
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# todo: 过程写的有问题，暂时不想改
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with tqdm(total=n_generation) as pbar:
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pbar.set_description('hybrid genetic process')
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for _ in range(n_generation):
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# 交叉
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for pop in range(population_size):
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if pop % 2 == 0 and np.random.random() < crossover_rate:
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index1, index2 = roulette_wheel_selection(pop_val), -1
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while True:
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index2 = roulette_wheel_selection(pop_val)
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if index1 != index2:
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break
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# 两点交叉算子
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offspring1, offspring2 = cycle_crossover(pop_individual[index1], pop_individual[index2])
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_, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, offspring1)
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pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
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pop_individual.append(offspring1)
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_, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, offspring2)
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pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
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pop_individual.append(offspring2)
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sigma_scaling(pop_val, 1)
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# 变异
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if np.random.random() < mutation_rate:
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index_ = roulette_wheel_selection(pop_val)
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offspring = swap_mutation(pop_individual[index_])
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_, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, offspring)
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pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
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pop_individual.append(offspring)
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sigma_scaling(pop_val, 1)
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new_population, new_popval = [], []
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for index in get_top_k_value(pop_val, population_size):
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new_population.append(pop_individual[index])
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new_popval.append(pop_val[index])
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pop_individual, pop_val = new_population, new_popval
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# select the best individual
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pop = np.argmin(pop_val)
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component_result, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, pop_individual[pop])
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placement_result, head_sequence = greedy_placement_route_generation(component_data, pcb_data, component_result,
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cycle_result, feeder_slot_result)
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return component_result, cycle_result, feeder_slot_result, placement_result, head_sequence
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def convert_individual_2_result(component_points, pop):
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component_result, cycle_result, feeder_slot_result = [], [], []
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feeder_part = [-1] * (max_slot_index // 2) # 已安装在供料器基座上的元件（0: 未分配）
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feeder_base_points = [0] * (max_slot_index // 2) # 供料器基座结余贴装点数量
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# 将基因信息转换为供料器基座安装结果
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for idx, gene in enumerate(pop):
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if idx >= len(component_points):
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break
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feeder_part[gene], feeder_base_points[gene] = idx, component_points[idx]
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# TODO: 暂时未考虑可用吸嘴数的限制
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# for _ in range(math.ceil(sum(component_points) / max_head_index)):
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while True:
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# === 周期内循环 ===
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assigned_part = [-1 for _ in range(max_head_index)] # 当前扫描到的头分配元件信息
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assigned_slot = [-1 for _ in range(max_head_index)] # 当前扫描到的供料器分配信息
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prev_scan_slot = len(feeder_part) // 2 # 前一轮扫描的位置
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while True:
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best_scan_part, best_scan_slot = [-1 for _ in range(max_head_index)], [-1 for _ in range(max_head_index)]
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best_slot_index = -1
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for slot in range(max_slot_index // 2 - (max_head_index - 1) * interval_ratio):
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scan_part, scan_slot = assigned_part.copy(), assigned_slot.copy()
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for head in range(max_head_index):
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part = feeder_part[slot + head * interval_ratio]
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# 贴装头和拾取槽位满足对应关系
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if scan_part[head] == -1 and part != -1 and feeder_base_points[slot + head * interval_ratio] > 0:
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scan_part[head], scan_slot[head] = part, slot + head * interval_ratio + 1
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if scan_part.count(-1) < best_scan_part.count(-1) or (scan_part.count(-1) == best_scan_part.count(-1)
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and abs(slot - prev_scan_slot) <
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abs(best_slot_index - prev_scan_slot)):
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best_slot_index = slot
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best_scan_part, best_scan_slot = scan_part.copy(), scan_slot.copy()
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assigned_points = 0
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for idx, slot in enumerate(best_scan_slot):
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if slot != -1 and assigned_slot[idx] == -1:
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feeder_base_points[slot - 1] -= 1
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assigned_points += 1
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assigned_part, assigned_slot = best_scan_part.copy(), best_scan_slot.copy()
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prev_scan_slot = best_slot_index
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if assigned_part.count(-1) == 0 or assigned_points == 0:
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break
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if len(cycle_result) == 0 or component_result[-1] != assigned_part:
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cycle_result.append(1)
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component_result.append(assigned_part)
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feeder_slot_result.append(assigned_slot)
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else:
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cycle_result[-1] += 1
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if sum(feeder_base_points) == 0:
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break
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return component_result, cycle_result, feeder_slot_result
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def feeder_arrange_evaluate(feeder_slot_result, cycle_result):
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assert len(feeder_slot_result) == len(cycle_result)
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arrange_val = 0
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for cycle, feeder_slot in enumerate(feeder_slot_result):
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pick_slot = set()
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for head, slot in enumerate(feeder_slot):
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pick_slot.add(slot - head * interval_ratio)
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arrange_val += len(pick_slot) * t_pick * cycle_result[cycle]
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pick_slot = list(pick_slot)
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pick_slot.sort()
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arrange_val += axis_moving_time(pick_slot[0] - pick_slot[-1]) * cycle_result[cycle]
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return arrange_val
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