增加单机优化方法
This commit is contained in:
954
base_optimizer/optimizer_common.py
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954
base_optimizer/optimizer_common.py
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import copy
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import time
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import math
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import random
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import argparse
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import os
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import warnings
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import copy
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from functools import wraps
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from collections import defaultdict
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from tqdm import tqdm
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# 整线参数
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max_machine_index = 3
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# 时间参数
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T_pp, T_tr, T_nc = 2, 5, 25
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# 机器参数
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max_head_index, max_slot_index = 6, 120
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interval_ratio = 2
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slot_interval = 15
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head_interval = slot_interval * interval_ratio
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head_nozzle = ['' for _ in range(max_head_index)] # 头上已经分配吸嘴
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# 位置信息
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slotf1_pos, slotr1_pos = [-31.267, 44.], [807., 810.545] # F1(前基座最左侧)、R1(后基座最右侧)位置
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fix_camera_pos = [269.531, 694.823] # 固定相机位置
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anc_marker_pos = [336.457, 626.230] # ANC基准点位置
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stopper_pos = [635.150, 124.738] # 止档块位置
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# 算法权重参数
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e_nz_change, e_gang_pick = 4, 0.6
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# 电机参数
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head_rotary_velocity = 8e-5 # 贴装头R轴旋转时间
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x_max_velocity, y_max_velocity = 1.4, 1.2
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x_max_acceleration, y_max_acceleration = x_max_velocity / 0.079, y_max_velocity / 0.079
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# 不同种类供料器宽度
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feeder_width = {'SM8': (7.25, 7.25), 'SM12': (7.00, 20.00), 'SM16': (7.00, 22.00),
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'SM24': (7.00, 29.00), 'SM32': (7.00, 44.00)}
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# 可用吸嘴数量限制
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nozzle_limit = {'CN065': 6, 'CN040': 6, 'CN220': 6, 'CN400': 6, 'CN140': 6}
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def axis_moving_time(distance, axis=0):
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distance = abs(distance) * 1e-3
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Lamax = x_max_velocity ** 2 / x_max_acceleration if axis == 0 else y_max_velocity ** 2 / y_max_acceleration
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Tmax = x_max_velocity / x_max_acceleration if axis == 0 else y_max_velocity / y_max_acceleration
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if axis == 0:
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return 2 * math.sqrt(distance / x_max_acceleration) if distance < Lamax else 2 * Tmax + (
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distance - Lamax) / x_max_velocity
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else:
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return 2 * math.sqrt(distance / y_max_acceleration) if distance < Lamax else 2 * Tmax + (
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distance - Lamax) / y_max_velocity
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def head_rotary_time(angle):
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while -180 > angle > 180:
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if angle > 180:
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angle -= 360
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else:
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angle += 360
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return abs(angle) * head_rotary_velocity
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def find_commonpart(head_group, feeder_group):
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feeder_group_len = len(feeder_group)
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max_length, max_common_part = -1, []
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for offset in range(-max_head_index + 1, feeder_group_len - 1):
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# offset: head_group相对于feeder_group的偏移量
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length, common_part = 0, []
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for hd_index in range(max_head_index):
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fd_index = hd_index + offset
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if fd_index < 0 or fd_index >= feeder_group_len:
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common_part.append(-1)
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continue
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if head_group[hd_index] == feeder_group[fd_index] and head_group[hd_index] != -1:
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length += 1
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common_part.append(head_group[hd_index])
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else:
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common_part.append(-1)
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if length > max_length:
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max_length = length
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max_common_part = common_part
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return max_common_part
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def timer_wrapper(func):
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@wraps(func)
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def measure_time(*args, **kwargs):
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start_time = time.time()
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result = func(*args, **kwargs)
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print("function {} running time : {} s".format(func.__name__, time.time() - start_time))
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return result
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return measure_time
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def feeder_assignment(component_data, pcb_data, component_result, cycle_result):
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# Section: 供料器分配结果
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feeder_slot_result, feeder_group_result = [], []
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feeder_limit = defaultdict(int)
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for component in range(len(component_data)):
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feeder_limit[component] = component_data.loc[component]['feeder-limit']
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for component_cycle in component_result:
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new_feeder_group = []
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for component in component_cycle:
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if component == -1 or feeder_limit[component] == 0 or new_feeder_group.count(component) >= feeder_limit[component]:
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new_feeder_group.append(-1)
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else:
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new_feeder_group.append(component)
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if len(new_feeder_group) == 0:
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continue
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while sum(i >= 0 for i in new_feeder_group) != 0:
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max_common_part, index = [], -1
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max_common_length = -1
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for feeder_index in range(len(feeder_group_result)):
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common_part = find_commonpart(new_feeder_group, feeder_group_result[feeder_index])
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if sum(i > 0 for i in common_part) > max_common_length:
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max_common_length = sum(i > 0 for i in common_part)
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max_common_part, index = common_part, feeder_index
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new_feeder_length = 0
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for feeder in new_feeder_group:
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if feeder != -1 and feeder_limit[feeder] > 0:
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new_feeder_length += 1
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if new_feeder_length > max_common_length:
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# 新分配供料器
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feeder_group_result.append([])
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for feeder_index in range(len(new_feeder_group)):
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feeder = new_feeder_group[feeder_index]
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if feeder != -1 and feeder_limit[feeder] > 0:
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feeder_group_result[-1].append(feeder)
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new_feeder_group[feeder_index] = -1
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feeder_limit[feeder] -= 1
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else:
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feeder_group_result[-1].append(-1)
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else:
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# 使用旧供料器
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for feeder_index, feeder_part in enumerate(max_common_part):
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if feeder_part != -1:
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new_feeder_group[feeder_index] = -1
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# 去除多余的元素
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for feeder_group in feeder_group_result:
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while len(feeder_group) > 0 and feeder_group[0] == -1:
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feeder_group.pop(0)
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while len(feeder_group) > 0 and feeder_group[-1] == -1:
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feeder_group.pop(-1)
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# 确定供料器组的安装位置
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point_num = len(pcb_data)
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component_pos = [[] for _ in range(len(component_data))]
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for point_cnt in range(point_num):
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part = pcb_data.loc[point_cnt, 'part']
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index = np.where(component_data['part'].values == part)[0]
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component_pos[index[0]].append(pcb_data.loc[point_cnt, 'x'] + stopper_pos[0])
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# 元件使用的头
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CT_Head = defaultdict(list)
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for component_cycle in component_result:
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for head, component in enumerate(component_cycle):
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if component == -1:
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continue
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if component not in CT_Head:
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CT_Head[component] = [head, head]
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CT_Head[component][0] = min(CT_Head[component][0], head)
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CT_Head[component][1] = max(CT_Head[component][1], head)
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# 供料器组分配的优先顺序
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feeder_assign_sequence = []
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for i in range(len(feeder_group_result)):
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for j in range(len(feeder_group_result)):
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if j in feeder_assign_sequence:
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continue
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if len(feeder_assign_sequence) == i:
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feeder_assign_sequence.append(j)
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else:
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seq = feeder_assign_sequence[-1]
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if cycle_result[seq] * len([k for k in feeder_group_result[seq] if k >= 0]) < cycle_result[j] * len(
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[k for k in feeder_group_result[seq] if k >= 0]):
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feeder_assign_sequence.pop(-1)
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feeder_assign_sequence.append(j)
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# TODO: 暂未考虑机械限位
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feeder_group_slot = [-1] * len(feeder_group_result)
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feeder_lane_state = [0] * max_slot_index # 0表示空，1表示已占有
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for index in feeder_assign_sequence:
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feeder_group = feeder_group_result[index]
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best_slot = []
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for cp_index, component in enumerate(feeder_group):
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if component == -1:
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continue
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best_slot.append(round((sum(component_pos[component]) / len(component_pos[component]) - slotf1_pos[
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0]) / slot_interval) + 1 - cp_index * interval_ratio)
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best_slot = round(sum(best_slot) / len(best_slot))
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search_dir, step = 0, 0 # dir: 1-向右, 0-向左
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left_out_range, right_out_range = False, False
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while True:
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assign_slot = best_slot + step if search_dir else best_slot - step
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# 出现越界，反向搜索
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if assign_slot + (len(feeder_group) - 1) * interval_ratio >= max_slot_index / 2:
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right_out_range = True
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search_dir = 0
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step += 1
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elif assign_slot < 0:
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left_out_range = True
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search_dir = 1
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step += 1
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else:
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if left_out_range or right_out_range:
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step += 1 # 单向搜索
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else:
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search_dir = 1 - search_dir # 双向搜索
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if search_dir == 0:
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step += 1
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assign_available = True
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# === 分配对应槽位 ===
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for slot in range(assign_slot, assign_slot + interval_ratio * len(feeder_group), interval_ratio):
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feeder_index = int((slot - assign_slot) / interval_ratio)
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pick_part = feeder_group[feeder_index]
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if feeder_lane_state[slot] == 1 and pick_part != -1:
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assign_available = False
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break
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if pick_part != -1 and (slot - CT_Head[pick_part][0] * interval_ratio <= 0 or
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slot + (max_head_index - CT_Head[pick_part][1] - 1) * interval_ratio > max_slot_index // 2):
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assign_available = False
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break
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if assign_available:
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for idx, part in enumerate(feeder_group):
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if part != -1:
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feeder_lane_state[assign_slot + idx * interval_ratio] = 1
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feeder_group_slot[index] = assign_slot
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break
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if feeder_group_slot[index] == -1:
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raise Exception('feeder assign error!')
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# 按照最大匹配原则，确定各元件周期拾取槽位
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for component_cycle in component_result:
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feeder_slot_result.append([-1] * max_head_index)
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head_index = [head for head, component in enumerate(component_cycle) if component >= 0]
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while head_index:
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max_overlap_counter = 0
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overlap_feeder_group_index, overlap_feeder_group_offset = -1, -1
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for feeder_group_idx, feeder_group in enumerate(feeder_group_result):
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# offset 头1 相对于 供料器组第一个元件的偏移量
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for offset in range(-max_head_index + 1, max_head_index + len(feeder_group)):
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overlap_counter = 0
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for head in head_index:
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if 0 <= head + offset < len(feeder_group) and component_cycle[head] == \
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feeder_group[head + offset]:
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overlap_counter += 1
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if overlap_counter > max_overlap_counter:
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max_overlap_counter = overlap_counter
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overlap_feeder_group_index, overlap_feeder_group_offset = feeder_group_idx, offset
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feeder_group = feeder_group_result[overlap_feeder_group_index]
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head_index_cpy = copy.deepcopy(head_index)
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for idx, head in enumerate(head_index_cpy):
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if 0 <= head + overlap_feeder_group_offset < len(feeder_group) and component_cycle[head] == \
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feeder_group[head + overlap_feeder_group_offset]:
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feeder_slot_result[-1][head] = feeder_group_slot[overlap_feeder_group_index] + interval_ratio * (
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head + overlap_feeder_group_offset)
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head_index.remove(head)
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return feeder_slot_result
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def dynamic_programming_cycle_path(pcb_data, cycle_placement, assigned_feeder):
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head_sequence = []
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num_pos = sum([placement != -1 for placement in cycle_placement]) + 1
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pos, head_set = [], []
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feeder_set = set()
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for head, feeder in enumerate(assigned_feeder):
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if feeder == -1:
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continue
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head_set.append(head)
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placement = cycle_placement[head]
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if feeder != -1 and placement == -1:
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print(assigned_feeder)
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print(cycle_placement)
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pos.append([pcb_data.loc[placement]['x'] - head * head_interval + stopper_pos[0],
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pcb_data.loc[placement]['y'] + stopper_pos[1]])
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feeder_set.add(feeder - head * interval_ratio)
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pos.insert(0, [slotf1_pos[0] + ((min(list(feeder_set)) + max(list(feeder_set))) / 2 - 1) * slot_interval,
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slotf1_pos[1]])
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def get_distance(pos_1, pos_2):
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return math.sqrt((pos_1[0] - pos_2[0]) ** 2 + (pos_1[1] - pos_2[1]) ** 2)
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# 各节点之间的距离
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dist = [[get_distance(pos_1, pos_2) for pos_2 in pos] for pos_1 in pos]
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min_dist = [[np.inf for _ in range(num_pos)] for s in range(1 << num_pos)]
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min_path = [[[] for _ in range(num_pos)] for s in range(1 << num_pos)]
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# 状压dp搜索
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for s in range(1, 1 << num_pos, 2):
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# 考虑节点集合s必须包括节点0
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if not (s & 1):
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continue
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for j in range(1, num_pos):
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# 终点j需在当前考虑节点集合s内
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if not (s & (1 << j)):
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continue
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if s == int((1 << j) | 1):
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# 若考虑节点集合s仅含节点0和节点j，dp边界，赋予初值
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# print('j:', j)
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min_path[s][j] = [j]
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min_dist[s][j] = dist[0][j]
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# 枚举下一个节点i，更新
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for i in range(1, num_pos):
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# 下一个节点i需在考虑节点集合s外
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if s & (1 << i):
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continue
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if min_dist[s][j] + dist[j][i] < min_dist[s | (1 << i)][i]:
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min_path[s | (1 << i)][i] = min_path[s][j] + [i]
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min_dist[s | (1 << i)][i] = min_dist[s][j] + dist[j][i]
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ans_dist = float('inf')
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ans_path = []
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# 求最终最短哈密顿回路
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for i in range(1, num_pos):
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if min_dist[(1 << num_pos) - 1][i] + dist[i][0] < ans_dist:
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# 更新，回路化
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ans_path = min_path[s][i]
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ans_dist = min_dist[(1 << num_pos) - 1][i] + dist[i][0]
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for parent in ans_path:
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head_sequence.append(head_set[parent - 1])
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start_head, end_head = head_sequence[0], head_sequence[-1]
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if pcb_data.loc[cycle_placement[start_head]]['x'] - start_head * head_interval > \
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pcb_data.loc[cycle_placement[end_head]]['x'] - end_head * head_interval:
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head_sequence = list(reversed(head_sequence))
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return head_sequence
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@timer_wrapper
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def greedy_placement_route_generation(component_data, pcb_data, component_result, cycle_result, feeder_slot_result):
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placement_result, head_sequence_result = [], []
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mount_point_index = [[] for _ in range(len(component_data))]
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mount_point_pos = [[] for _ in range(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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component_index = component_data[component_data['part'] == part].index.tolist()[0]
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# 记录贴装点序号索引和对应的位置坐标
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mount_point_index[component_index].append(i)
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mount_point_pos[component_index].append([pcb_data.loc[i]['x'], pcb_data.loc[i]['y']])
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search_dir = 1 # 0：自左向右搜索 1：自右向左搜索
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for cycle_set in range(len(component_result)):
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floor_cycle, ceil_cycle = sum(cycle_result[:cycle_set]), sum(cycle_result[:(cycle_set + 1)])
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for cycle in range(floor_cycle, ceil_cycle):
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# search_dir = 1 - search_dir
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assigned_placement = [-1] * max_head_index
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max_pos = [max(mount_point_pos[component_index], key=lambda x: x[0]) for component_index in
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range(len(mount_point_pos)) if len(mount_point_pos[component_index]) > 0][0][0]
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min_pos = [min(mount_point_pos[component_index], key=lambda x: x[0]) for component_index in
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range(len(mount_point_pos)) if len(mount_point_pos[component_index]) > 0][0][0]
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point2head_range = min(math.floor((max_pos - min_pos) / head_interval) + 1, max_head_index)
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# 最近邻确定
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way_point = None
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head_range = range(max_head_index - 1, -1, -1) if search_dir else range(max_head_index)
|
||||
for head_counter, head in enumerate(head_range):
|
||||
if component_result[cycle_set][head] == -1:
|
||||
continue
|
||||
|
||||
component_index = component_result[cycle_set][head]
|
||||
if way_point is None or head_counter % point2head_range == 0:
|
||||
index = 0
|
||||
if way_point is None:
|
||||
if search_dir:
|
||||
index = np.argmax(mount_point_pos[component_index], axis=0)[0]
|
||||
else:
|
||||
index = np.argmin(mount_point_pos[component_index], axis=0)[0]
|
||||
else:
|
||||
for next_head in head_range:
|
||||
component_index = component_result[cycle_set][next_head]
|
||||
if assigned_placement[next_head] == -1 and component_index != -1:
|
||||
num_points = len(mount_point_pos[component_index])
|
||||
index = np.argmin(
|
||||
[abs(mount_point_pos[component_index][i][0] - way_point[0]) * .1 + abs(
|
||||
mount_point_pos[component_index][i][1] - way_point[1]) for i in
|
||||
range(num_points)])
|
||||
head = next_head
|
||||
break
|
||||
# index = np.argmax(mount_point_pos[component_index], axis=0)[0]
|
||||
assigned_placement[head] = mount_point_index[component_index][index]
|
||||
|
||||
# 记录路标点
|
||||
way_point = mount_point_pos[component_index][index]
|
||||
way_point[0] += (max_head_index - head - 1) * head_interval if search_dir else -head * head_interval
|
||||
|
||||
mount_point_index[component_index].pop(index)
|
||||
mount_point_pos[component_index].pop(index)
|
||||
else:
|
||||
head_index, point_index = -1, -1
|
||||
min_cheby_distance, min_euler_distance = float('inf'), float('inf')
|
||||
for next_head in range(max_head_index):
|
||||
if assigned_placement[next_head] != -1 or component_result[cycle_set][next_head] == -1:
|
||||
continue
|
||||
next_comp_index = component_result[cycle_set][next_head]
|
||||
for counter in range(len(mount_point_pos[next_comp_index])):
|
||||
if search_dir:
|
||||
delta_x = abs(mount_point_pos[next_comp_index][counter][0] - way_point[0]
|
||||
+ (max_head_index - next_head - 1) * head_interval)
|
||||
else:
|
||||
delta_x = abs(mount_point_pos[next_comp_index][counter][0] - way_point[0]
|
||||
- next_head * head_interval)
|
||||
|
||||
delta_y = abs(mount_point_pos[next_comp_index][counter][1] - way_point[1])
|
||||
|
||||
euler_distance = pow(axis_moving_time(delta_x, 0), 2) + pow(axis_moving_time(delta_y, 1), 2)
|
||||
cheby_distance = max(axis_moving_time(delta_x, 0),
|
||||
axis_moving_time(delta_y, 1)) + 5e-2 * euler_distance
|
||||
if cheby_distance < min_cheby_distance or (abs(cheby_distance - min_cheby_distance) < 1e-9
|
||||
and euler_distance < min_euler_distance):
|
||||
# if euler_distance < min_euler_distance:
|
||||
min_cheby_distance, min_euler_distance = cheby_distance, euler_distance
|
||||
head_index, point_index = next_head, counter
|
||||
|
||||
component_index = component_result[cycle_set][head_index]
|
||||
assert (0 <= head_index < max_head_index)
|
||||
|
||||
assigned_placement[head_index] = mount_point_index[component_index][point_index]
|
||||
way_point = mount_point_pos[component_index][point_index]
|
||||
way_point[0] += (max_head_index - head_index - 1) * head_interval if search_dir \
|
||||
else -head_index * head_interval
|
||||
|
||||
mount_point_index[component_index].pop(point_index)
|
||||
mount_point_pos[component_index].pop(point_index)
|
||||
|
||||
placement_result.append(assigned_placement) # 各个头上贴装的元件类型
|
||||
head_sequence_result.append(
|
||||
dynamic_programming_cycle_path(pcb_data, assigned_placement, feeder_slot_result[cycle_set]))
|
||||
|
||||
return placement_result, head_sequence_result
|
||||
|
||||
|
||||
@timer_wrapper
|
||||
def beam_search_for_route_generation(component_data, pcb_data, component_result, cycle_result, feeder_slot_result):
|
||||
beam_width = 4 # 集束宽度
|
||||
base_points = [float('inf'), float('inf')]
|
||||
|
||||
mount_point_index = [[] for _ in range(len(component_data))]
|
||||
mount_point_pos = [[] for _ in range(len(component_data))]
|
||||
|
||||
for i in range(len(pcb_data)):
|
||||
part = pcb_data.loc[i]['part']
|
||||
component_index = component_data[component_data['part'] == part].index.tolist()[0]
|
||||
|
||||
# 记录贴装点序号索引和对应的位置坐标
|
||||
mount_point_index[component_index].append(i)
|
||||
mount_point_pos[component_index].append([pcb_data.loc[i]['x'], pcb_data.loc[i]['y']])
|
||||
|
||||
# 记录最左下角坐标
|
||||
if mount_point_pos[component_index][-1][0] < base_points[0]:
|
||||
base_points[0] = mount_point_pos[component_index][-1][0]
|
||||
if mount_point_pos[component_index][-1][1] < base_points[1]:
|
||||
base_points[1] = mount_point_pos[component_index][-1][1]
|
||||
|
||||
beam_placement_sequence, beam_head_sequence = [], []
|
||||
beam_mount_point_index, beam_mount_point_pos = [], []
|
||||
|
||||
for beam_counter in range(beam_width):
|
||||
beam_mount_point_index.append(copy.deepcopy(mount_point_index))
|
||||
beam_mount_point_pos.append(copy.deepcopy(mount_point_pos))
|
||||
|
||||
beam_placement_sequence.append([])
|
||||
beam_head_sequence.append([])
|
||||
|
||||
beam_distance = [0 for _ in range(beam_width)] # 记录当前集束搜索点的点数
|
||||
def argpartition(list, kth):
|
||||
if kth < len(list):
|
||||
return np.argpartition(list, kth)
|
||||
else:
|
||||
index, indexes = 0, []
|
||||
while len(indexes) < kth:
|
||||
indexes.append(index)
|
||||
index += 1
|
||||
if index >= len(list):
|
||||
index = 0
|
||||
return np.array(indexes)
|
||||
|
||||
with tqdm(total=100) as pbar:
|
||||
search_dir = 0
|
||||
pbar.set_description('route schedule')
|
||||
for cycle_set in range(len(component_result)):
|
||||
floor_cycle, ceil_cycle = sum(cycle_result[:cycle_set]), sum(cycle_result[:(cycle_set + 1)])
|
||||
for cycle in range(floor_cycle, ceil_cycle):
|
||||
search_dir = 1 - search_dir
|
||||
beam_way_point = None
|
||||
for beam_counter in range(beam_width):
|
||||
beam_placement_sequence[beam_counter].append([-1 for _ in range(max_head_index)])
|
||||
|
||||
head_range = range(max_head_index - 1, -1, -1) if search_dir else range(max_head_index)
|
||||
for head in head_range:
|
||||
component_index = component_result[cycle_set][head]
|
||||
if component_index == -1:
|
||||
continue
|
||||
|
||||
if beam_way_point is None:
|
||||
# 首个贴装点的选取，距离基准点最近的beam_width个点
|
||||
beam_way_point = [[0, 0]] * beam_width
|
||||
|
||||
for beam_counter in range(beam_width):
|
||||
if search_dir:
|
||||
index = np.argmax(beam_mount_point_pos[beam_counter][component_index], axis=0)[0]
|
||||
else:
|
||||
index = np.argmin(beam_mount_point_pos[beam_counter][component_index], axis=0)[0]
|
||||
|
||||
beam_placement_sequence[beam_counter][-1][head] = beam_mount_point_index[beam_counter][component_index][index]
|
||||
|
||||
beam_way_point[beam_counter] = beam_mount_point_pos[beam_counter][component_index][index]
|
||||
beam_way_point[beam_counter][0] += (max_head_index - head - 1) * head_interval if \
|
||||
search_dir else -head * head_interval
|
||||
|
||||
beam_mount_point_index[beam_counter][component_index].pop(index)
|
||||
beam_mount_point_pos[beam_counter][component_index].pop(index)
|
||||
else:
|
||||
# 后续贴装点
|
||||
search_beam_distance = []
|
||||
search_beam_index = [0] * (beam_width ** 2)
|
||||
for beam_counter in range(beam_width ** 2):
|
||||
search_beam_distance.append(beam_distance[beam_counter // beam_width])
|
||||
|
||||
for beam_counter in range(beam_width):
|
||||
# 对于集束beam_counter + 1最近的beam_width个点
|
||||
num_points = len(beam_mount_point_pos[beam_counter][component_index])
|
||||
|
||||
dist = []
|
||||
for i in range(num_points):
|
||||
if search_dir:
|
||||
delta_x = axis_moving_time(
|
||||
beam_mount_point_pos[beam_counter][component_index][i][0] -
|
||||
beam_way_point[beam_counter][0] + (max_head_index - head - 1) * head_interval,
|
||||
0)
|
||||
else:
|
||||
delta_x = axis_moving_time(
|
||||
beam_mount_point_pos[beam_counter][component_index][i][0] -
|
||||
beam_way_point[beam_counter][0] - head * head_interval, 0)
|
||||
|
||||
delta_y = axis_moving_time(beam_mount_point_pos[beam_counter][component_index][i][1] -
|
||||
beam_way_point[beam_counter][1], 1)
|
||||
|
||||
dist.append(max(delta_x, delta_y))
|
||||
|
||||
indexes = argpartition(dist, kth=beam_width)[:beam_width]
|
||||
|
||||
# 记录中间信息
|
||||
for i, index in enumerate(indexes):
|
||||
search_beam_distance[i + beam_counter * beam_width] += dist[index]
|
||||
search_beam_index[i + beam_counter * beam_width] = index
|
||||
|
||||
indexes = np.argsort(search_beam_distance)
|
||||
|
||||
beam_mount_point_pos_cpy = copy.deepcopy(beam_mount_point_pos)
|
||||
beam_mount_point_index_cpy = copy.deepcopy(beam_mount_point_index)
|
||||
|
||||
beam_placement_sequence_cpy = copy.deepcopy(beam_placement_sequence)
|
||||
beam_head_sequence_cpy = copy.deepcopy(beam_head_sequence)
|
||||
beam_counter = 0
|
||||
assigned_placement = []
|
||||
|
||||
for i, index in enumerate(indexes):
|
||||
# 拷贝原始集束数据
|
||||
beam_mount_point_pos[beam_counter] = copy.deepcopy(beam_mount_point_pos_cpy[index // beam_width])
|
||||
beam_mount_point_index[beam_counter] = copy.deepcopy(beam_mount_point_index_cpy[index // beam_width])
|
||||
beam_placement_sequence[beam_counter] = copy.deepcopy(beam_placement_sequence_cpy[index // beam_width])
|
||||
beam_head_sequence[beam_counter] = copy.deepcopy(beam_head_sequence_cpy[index // beam_width])
|
||||
|
||||
# 更新各集束最新扫描的的贴装点
|
||||
component_index = component_result[cycle_set][head]
|
||||
|
||||
beam_placement_sequence[beam_counter][-1][head] = \
|
||||
beam_mount_point_index[beam_counter][component_index][search_beam_index[index]]
|
||||
|
||||
if beam_placement_sequence[beam_counter][
|
||||
-1] in assigned_placement and beam_width - beam_counter < len(indexes) - i:
|
||||
continue
|
||||
|
||||
assigned_placement.append(beam_placement_sequence[beam_counter][-1])
|
||||
|
||||
# 更新参考基准点
|
||||
beam_way_point[beam_counter] = beam_mount_point_pos[beam_counter][component_index][search_beam_index[index]]
|
||||
beam_way_point[beam_counter][0] += (max_head_index - head - 1) * head_interval if \
|
||||
search_dir else -head * head_interval
|
||||
|
||||
# 更新各集束贴装路径长度，移除各集束已分配的贴装点
|
||||
beam_distance[beam_counter] = search_beam_distance[index]
|
||||
|
||||
beam_mount_point_pos[beam_counter][component_index].pop(search_beam_index[index])
|
||||
beam_mount_point_index[beam_counter][component_index].pop(search_beam_index[index])
|
||||
|
||||
beam_counter += 1
|
||||
|
||||
if beam_counter >= beam_width:
|
||||
break
|
||||
assert(beam_counter >= beam_width)
|
||||
|
||||
# 更新头贴装顺序
|
||||
for beam_counter in range(beam_width):
|
||||
beam_head_sequence[beam_counter].append(
|
||||
dynamic_programming_cycle_path(pcb_data, beam_placement_sequence[beam_counter][-1],
|
||||
feeder_slot_result[cycle_set]))
|
||||
|
||||
pbar.update(1 / sum(cycle_result) * 100)
|
||||
|
||||
index = np.argmin(beam_distance)
|
||||
return beam_placement_sequence[index], beam_head_sequence[index]
|
||||
|
||||
|
||||
def optimal_nozzle_assignment(component_data, pcb_data):
|
||||
# === Nozzle Assignment ===
|
||||
# number of points for nozzle & number of heads for nozzle
|
||||
nozzle_points, nozzle_assigned_counter = defaultdict(int), defaultdict(int)
|
||||
if len(pcb_data) == 0:
|
||||
return nozzle_assigned_counter
|
||||
for _, step in pcb_data.iterrows():
|
||||
part = step['part']
|
||||
idx = component_data[component_data['part'] == part].index.tolist()[0]
|
||||
nozzle = component_data.loc[idx]['nz']
|
||||
|
||||
nozzle_assigned_counter[nozzle] = 0
|
||||
nozzle_points[nozzle] += 1
|
||||
|
||||
assert len(nozzle_points.keys()) <= max_head_index
|
||||
total_points, available_head = len(pcb_data), max_head_index
|
||||
# S1: set of nozzle types which are sufficient to assign one nozzle to the heads
|
||||
# S2: temporary nozzle set
|
||||
# S3: set of nozzle types which already have the maximum reasonable nozzle amounts.
|
||||
S1, S2, S3 = [], [], []
|
||||
|
||||
for nozzle in nozzle_points.keys(): # Phase 1
|
||||
if nozzle_points[nozzle] * max_head_index < total_points:
|
||||
nozzle_assigned_counter[nozzle] = 1
|
||||
available_head -= 1
|
||||
total_points -= nozzle_points[nozzle]
|
||||
|
||||
S1.append(nozzle)
|
||||
else:
|
||||
S2.append(nozzle)
|
||||
|
||||
available_head_ = available_head # Phase 2
|
||||
for nozzle in S2:
|
||||
nozzle_assigned_counter[nozzle] = math.floor(available_head * nozzle_points[nozzle] / total_points)
|
||||
available_head_ = available_head_ - nozzle_assigned_counter[nozzle]
|
||||
|
||||
S2.sort(key=lambda x: nozzle_points[x] / (nozzle_assigned_counter[x] + 1e-10), reverse=True)
|
||||
while available_head_ > 0:
|
||||
nozzle = S2[0]
|
||||
nozzle_assigned_counter[nozzle] += 1
|
||||
|
||||
S2.remove(nozzle)
|
||||
S3.append(nozzle)
|
||||
available_head_ -= 1
|
||||
|
||||
phase_iteration = len(S2) - 1
|
||||
while phase_iteration > 0: # Phase 3
|
||||
nozzle_i_val, nozzle_j_val = 0, 0
|
||||
nozzle_i, nozzle_j = None, None
|
||||
for nozzle in S2:
|
||||
if nozzle_i is None or nozzle_points[nozzle] / nozzle_assigned_counter[nozzle] > nozzle_i_val:
|
||||
nozzle_i_val = nozzle_points[nozzle] / nozzle_assigned_counter[nozzle]
|
||||
nozzle_i = nozzle
|
||||
|
||||
if nozzle_assigned_counter[nozzle] > 1:
|
||||
if nozzle_j is None or nozzle_points[nozzle] / (nozzle_assigned_counter[nozzle] - 1) < nozzle_j_val:
|
||||
nozzle_j_val = nozzle_points[nozzle] / (nozzle_assigned_counter[nozzle] - 1)
|
||||
nozzle_j = nozzle
|
||||
|
||||
if nozzle_i and nozzle_j and nozzle_points[nozzle_j] / (nozzle_assigned_counter[nozzle_j] - 1) < \
|
||||
nozzle_points[nozzle_i] / nozzle_assigned_counter[nozzle_i]:
|
||||
nozzle_assigned_counter[nozzle_j] -= 1
|
||||
nozzle_assigned_counter[nozzle_i] += 1
|
||||
S2.remove(nozzle_i)
|
||||
S3.append(nozzle_i)
|
||||
else:
|
||||
break
|
||||
|
||||
return nozzle_assigned_counter
|
||||
|
||||
|
||||
# === 遗传算法公用函数 ===
|
||||
def sigma_scaling(pop_val, c: float):
|
||||
# function: f' = max(f - (avg(f) - c · sigma(f), 0)
|
||||
avg_val = sum(pop_val) / len(pop_val)
|
||||
sigma_val = math.sqrt(sum(abs(v - avg_val) for v in pop_val) / len(pop_val))
|
||||
|
||||
for idx, val in enumerate(pop_val):
|
||||
pop_val[idx] = max(val - (avg_val - c * sigma_val), 0)
|
||||
return pop_val
|
||||
|
||||
|
||||
def directed_edge_recombination_crossover(c, individual1, individual2):
|
||||
assert len(individual1) == len(individual2)
|
||||
left_edge_list, right_edge_list = defaultdict(list), defaultdict(list)
|
||||
|
||||
for index in range(len(individual1) - 1):
|
||||
elem1, elem2 = individual1[index], individual1[index + 1]
|
||||
right_edge_list[elem1].append(elem2)
|
||||
left_edge_list[elem2].append(elem1)
|
||||
|
||||
for index in range(len(individual2) - 1):
|
||||
elem1, elem2 = individual2[index], individual2[index + 1]
|
||||
right_edge_list[elem1].append(elem2)
|
||||
left_edge_list[elem2].append(elem1)
|
||||
|
||||
offspring = []
|
||||
while len(offspring) != len(individual1):
|
||||
while True:
|
||||
center_element = np.random.choice(individual1)
|
||||
if center_element not in offspring: # 避免重复选取
|
||||
break
|
||||
direction, candidate = 1, [center_element]
|
||||
parent = center_element
|
||||
for edge_list in left_edge_list.values():
|
||||
while parent in edge_list:
|
||||
edge_list.remove(parent)
|
||||
|
||||
for edge_list in right_edge_list.values():
|
||||
while parent in edge_list:
|
||||
edge_list.remove(parent)
|
||||
|
||||
while True:
|
||||
max_len, max_len_neighbor = -1, 0
|
||||
if direction == 1:
|
||||
if len(right_edge_list[parent]) == 0:
|
||||
direction, parent = -1, center_element
|
||||
continue
|
||||
for neighbor in right_edge_list[parent]:
|
||||
if max_len < len(right_edge_list[neighbor]):
|
||||
max_len_neighbor = neighbor
|
||||
max_len = len(right_edge_list[neighbor])
|
||||
candidate.append(max_len_neighbor)
|
||||
parent = max_len_neighbor
|
||||
elif direction == -1:
|
||||
if len(left_edge_list[parent]) == 0:
|
||||
direction, parent = 0, center_element
|
||||
continue
|
||||
for neighbor in left_edge_list[parent]:
|
||||
if max_len < len(left_edge_list[neighbor]):
|
||||
max_len_neighbor = neighbor
|
||||
max_len = len(left_edge_list[neighbor])
|
||||
candidate.insert(0, max_len_neighbor)
|
||||
parent = max_len_neighbor
|
||||
else:
|
||||
break
|
||||
|
||||
# 移除重复元素
|
||||
for edge_list in left_edge_list.values():
|
||||
while max_len_neighbor in edge_list:
|
||||
edge_list.remove(max_len_neighbor)
|
||||
|
||||
for edge_list in right_edge_list.values():
|
||||
while max_len_neighbor in edge_list:
|
||||
edge_list.remove(max_len_neighbor)
|
||||
|
||||
offspring += candidate
|
||||
|
||||
return offspring
|
||||
|
||||
|
||||
def partially_mapped_crossover(parent1, parent2):
|
||||
range_ = np.random.randint(0, len(parent1), 2) # 前闭后开
|
||||
range_ = sorted(range_)
|
||||
|
||||
parent1_cpy, parent2_cpy = [-1 for _ in range(len(parent1))], [-1 for _ in range(len(parent2))]
|
||||
|
||||
parent1_cpy[range_[0]: range_[1] + 1] = copy.deepcopy(parent2[range_[0]: range_[1] + 1])
|
||||
parent2_cpy[range_[0]: range_[1] + 1] = copy.deepcopy(parent1[range_[0]: range_[1] + 1])
|
||||
|
||||
for index in range(len(parent1)):
|
||||
if range_[0] <= index <= range_[1]:
|
||||
continue
|
||||
|
||||
cur_ptr, cur_elem = 0, parent1[index]
|
||||
while True:
|
||||
parent1_cpy[index] = cur_elem
|
||||
if parent1_cpy.count(cur_elem) == 1:
|
||||
break
|
||||
parent1_cpy[index] = -1
|
||||
|
||||
if cur_ptr == 0:
|
||||
cur_ptr, cur_elem = 1, parent2[index]
|
||||
else:
|
||||
index_ = parent1_cpy.index(cur_elem)
|
||||
cur_elem = parent2[index_]
|
||||
|
||||
for index in range(len(parent2)):
|
||||
if range_[0] <= index <= range_[1]:
|
||||
continue
|
||||
|
||||
cur_ptr, cur_elem = 0, parent2[index]
|
||||
while True:
|
||||
parent2_cpy[index] = cur_elem
|
||||
if parent2_cpy.count(cur_elem) == 1:
|
||||
break
|
||||
parent2_cpy[index] = -1
|
||||
|
||||
if cur_ptr == 0:
|
||||
cur_ptr, cur_elem = 1, parent1[index]
|
||||
else:
|
||||
index_ = parent2_cpy.index(cur_elem)
|
||||
cur_elem = parent1[index_]
|
||||
|
||||
return parent1_cpy, parent2_cpy
|
||||
|
||||
|
||||
def cycle_crossover(parent1, parent2):
|
||||
offspring1, offspring2 = [-1 for _ in range(len(parent1))], [-1 for _ in range(len(parent2))]
|
||||
|
||||
idx = 0
|
||||
while True:
|
||||
if offspring1[idx] != -1:
|
||||
break
|
||||
offspring1[idx] = parent1[idx]
|
||||
idx = parent1.index(parent2[idx])
|
||||
|
||||
for idx, gene in enumerate(offspring1):
|
||||
if gene == -1:
|
||||
offspring1[idx] = parent2[idx]
|
||||
|
||||
idx = 0
|
||||
while True:
|
||||
if offspring2[idx] != -1:
|
||||
break
|
||||
offspring2[idx] = parent2[idx]
|
||||
idx = parent2.index(parent1[idx])
|
||||
|
||||
for idx, gene in enumerate(offspring2):
|
||||
if gene == -1:
|
||||
offspring2[idx] = parent1[idx]
|
||||
|
||||
return offspring1, offspring2
|
||||
|
||||
|
||||
def swap_mutation(parent):
|
||||
range_ = np.random.randint(0, len(parent), 2)
|
||||
parent[range_[0]], parent[range_[1]] = parent[range_[1]], parent[range_[0]]
|
||||
return parent
|
||||
|
||||
|
||||
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():
|
||||
if component_index == 0:
|
||||
while True:
|
||||
index1, index2 = random.sample(range(points + max_machine_index - 2), 2)
|
||||
if offspring[idx + index1] != offspring[idx + index2]:
|
||||
break
|
||||
|
||||
clip = offspring[idx: idx + points + max_machine_index - 1].copy()
|
||||
avl_machine = 0
|
||||
for idx_, gene in enumerate(clip):
|
||||
if gene == 0 and (idx_ == 0 or clip[idx_ - 1] != 0):
|
||||
avl_machine += 1
|
||||
|
||||
clip[index1], clip[index2] = clip[index2], clip[index1]
|
||||
for idx_, gene in enumerate(clip):
|
||||
if gene == 0 and (idx_ == 0 or clip[idx_ - 1] != 0):
|
||||
avl_machine -= 1
|
||||
|
||||
if avl_machine != 0:
|
||||
return offspring
|
||||
|
||||
offspring[idx + index1], offspring[idx + index2] = offspring[idx + index2], offspring[idx + index1]
|
||||
break
|
||||
|
||||
component_index -= 1
|
||||
idx += (points + max_machine_index - 1)
|
||||
|
||||
return offspring
|
||||
|
||||
|
||||
def random_selective(data, possibility): # 依概率选择随机数
|
||||
assert len(data) == len(possibility) and len(data) > 0
|
||||
|
||||
sum_val = sum(possibility)
|
||||
possibility = [p / sum_val for p in possibility]
|
||||
|
||||
random_val = random.random()
|
||||
for idx, val in enumerate(possibility):
|
||||
random_val -= val
|
||||
if random_val <= 0:
|
||||
break
|
||||
return data[idx]
|
||||
|
||||
|
||||
def insert_mutation(parent):
|
||||
pos, val = np.random.randint(0, len(parent), 1), parent[-1]
|
||||
parent[pos: len(parent) - 1] = parent[pos + 1:]
|
||||
parent[pos] = val
|
||||
return parent
|
||||
|
||||
|
||||
def roulette_wheel_selection(pop_eval):
|
||||
# Roulette wheel
|
||||
random_val = np.random.random() * sum(pop_eval)
|
||||
for idx, val in enumerate(pop_eval):
|
||||
random_val -= val
|
||||
if random_val <= 0:
|
||||
return idx
|
||||
return len(pop_eval) - 1
|
||||
|
||||
|
||||
def get_top_k_value(pop_val, k: int, reverse=True):
|
||||
res = []
|
||||
pop_val_cpy = copy.deepcopy(pop_val)
|
||||
pop_val_cpy.sort(reverse=reverse)
|
||||
|
||||
for i in range(min(len(pop_val_cpy), k)):
|
||||
for j in range(len(pop_val)):
|
||||
if abs(pop_val_cpy[i] - pop_val[j]) < 1e-9 and j not in res:
|
||||
res.append(j)
|
||||
break
|
||||
return res
|
||||
Reference in New Issue
Block a user