205 lines
9.7 KiB
Python
205 lines
9.7 KiB
Python
from base_optimizer.optimizer_common import *
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from result_analysis import *
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def convert_cell_2_result(pcb_data, component_data, component_cell, population):
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assert component_cell['points'].sum() == len(pcb_data)
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head_assignment = [[] for _ in range(max_head_index)]
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wl = [0 for _ in range(max_head_index)] # workload
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e1, e2, e3 = 1, 0.5, 1. / 6
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component_result, cycle_result, feeder_slot_result = [], [], []
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for index in population:
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if component_cell.loc[index]['points'] == 0:
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continue
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# 元胞对应的元件类型和贴装点数
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component_type, component_points = component_cell.loc[index, 'index'], component_cell.loc[index, 'points']
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nozzle_change, maxwl = [0 for _ in range(max_head_index)], [0 for _ in range(max_head_index)]
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for head in range(max_head_index):
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if head_assignment[head]:
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assigned_part = head_assignment[head][-1][0]
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if component_data.loc[assigned_part]['nz'] != component_data.loc[component_type]['nz']:
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nozzle_change[head] = 1
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wl1 = wl.copy()
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wl1[head] += component_points
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maxwl[head] = max(wl1) + e1 * nozzle_change[head]
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awl, wl2 = min(maxwl), wl.copy()
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for idx, val in enumerate(maxwl):
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if val > awl:
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wl2[idx] += e3
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head_ = wl2.index(min(wl2))
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wl[head_] += component_points
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head_assignment[head_].append([component_type, component_points])
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head_assignment_counter = [0 for _ in range(max_head_index)]
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while True:
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assigned_part, assigned_cycle = [-1 for _ in range(max_head_index)], [0 for _ in range(max_head_index)]
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for head in range(max_head_index):
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counter = head_assignment_counter[head]
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if head_assignment[head] and head_assignment[head][counter][1] > 0:
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assigned_part[head] = head_assignment[head][counter][0]
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assigned_cycle[head] = head_assignment[head][counter][1]
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nonzero_cycle = [cycle for cycle in assigned_cycle if cycle > 0]
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if not nonzero_cycle:
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break
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cycle = min(nonzero_cycle)
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cycle_result.append(cycle)
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component_result.append(assigned_part)
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for head in range(max_head_index):
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counter = head_assignment_counter[head]
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if head_assignment[head] and head_assignment[head][counter][1] > 0:
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head_assignment[head][counter][1] -= cycle_result[-1]
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if head_assignment[head][counter][1] == 0 and counter < len(head_assignment[head]) - 1:
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head_assignment_counter[head] += 1
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feeder_slot_result = feeder_assignment(component_data, pcb_data, component_result, cycle_result)
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return component_result, cycle_result, feeder_slot_result
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@timer_wrapper
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def optimizer_celldivision(pcb_data, component_data, hinter=True):
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# Crossover method: Two-point crossover
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# Mutation method: Swap
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# Parent selection method: Roulette wheel
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# Termination condition: 20 successive non-improvement iterations
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population_size = 40 # 种群规模
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crossover_rate, mutation_rate = .6, .02
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golden_section = 0.618
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# 获取元件元胞
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point_num = len(pcb_data)
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component_cell = pd.DataFrame({'index': np.arange(len(component_data)), 'points': np.zeros(len(component_data), dtype=int)})
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for point_cnt in range(point_num):
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part = pcb_data.loc[point_cnt, 'fdr'].split(' ', 1)[1]
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index = np.where(component_data['part'].values == part)
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component_cell.loc[index[0], 'points'] += 1
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component_cell = component_cell[~component_cell['points'].isin([0])]
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# component_cell.sort_values(by = "points" , inplace = True, ascending = False)
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best_population, best_component_cell = [], []
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min_pop_val = float('inf') # 最优种群价值
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Div, Imp = 0, 0
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while True:
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# randomly generate permutations
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generation_ = np.array(component_cell.index)
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pop_generation = []
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for _ in range(population_size):
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np.random.shuffle(generation_)
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pop_generation.append(generation_.tolist())
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pop_val = []
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for pop in range(population_size):
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component_result, cycle_result, feeder_slot_result = convert_cell_2_result(pcb_data, component_data,
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component_cell,
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pop_generation[pop])
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pop_val.append(
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component_assign_evaluate(component_data, component_result, cycle_result, feeder_slot_result))
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# 初始化随机生成种群
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Upit = int(1.5 * np.sqrt(len(component_cell)))
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while Div < Upit:
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if hinter:
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print('----- current div : ' + str(Div) + ' , total div : ' + str(Upit) + ' -----')
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# 选择
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new_pop_generation, new_pop_val = [], []
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top_k_index = get_top_k_value(pop_val, int(population_size * 0.3))
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for index in top_k_index:
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new_pop_generation.append(pop_generation[index])
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new_pop_val.append(pop_val[index])
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index = [i for i in range(population_size)]
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select_index = random.choices(index, weights=pop_val, k=population_size - int(population_size * 0.3))
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for index in select_index:
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new_pop_generation.append(pop_generation[index])
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new_pop_val.append(pop_val[index])
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pop_generation, pop_val = new_pop_generation, new_pop_val
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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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pop_generation[index1], pop_generation[index2] = partially_mapped_crossover(pop_generation[index1],
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pop_generation[index2])
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if np.random.random() < mutation_rate:
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index_ = roulette_wheel_selection(pop_val)
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swap_mutation(pop_generation[index_])
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# 将元件元胞分配到各个吸杆上，计算价值函数
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for pop in range(population_size):
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component_result, cycle_result, feeder_slot_result = convert_cell_2_result(pcb_data, component_data,
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component_cell,
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pop_generation[pop])
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pop_val[pop] = component_assign_evaluate(component_data, component_result, cycle_result,
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feeder_slot_result)
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assert(pop_val[pop] > 0)
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if min(pop_val) < min_pop_val:
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min_pop_val = min(pop_val)
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best_population = copy.deepcopy(pop_generation[np.argmin(pop_val)])
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best_component_cell = copy.deepcopy(component_cell)
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Div, Imp = 0, 1
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else:
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Div += 1
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if Imp == 1:
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Div, Imp = 0, 0
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# Section: cell division operation
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if hinter:
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print(' ------------- cell division operation ------------- ')
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division_component_cell = pd.DataFrame()
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for idx, rows in component_cell.iterrows():
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if component_cell.loc[idx, 'points'] <= 1:
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division_component_cell = pd.concat([division_component_cell, pd.DataFrame([rows])],
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ignore_index=True)
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else:
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division_component_cell = pd.concat([division_component_cell, pd.DataFrame([rows] * 2)],
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ignore_index=True)
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rows_counter = len(division_component_cell)
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division_points = int(max(np.ceil(division_component_cell.loc[rows_counter - 2,
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'points'] * golden_section), 1))
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# 避免出现空元胞的情形
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if division_points == 0 or division_points == division_component_cell.loc[
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rows_counter - 2, 'points']:
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division_component_cell.loc[rows_counter - 2, 'points'] = 1
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else:
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division_component_cell.loc[rows_counter - 2, 'points'] = division_points
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division_component_cell.loc[rows_counter - 1, 'points'] -= division_component_cell.loc[
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rows_counter - 2, 'points']
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if division_component_cell.loc[rows_counter - 2, 'points'] == 0 or division_component_cell.loc[
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rows_counter - 1, 'points'] == 0:
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raise ValueError
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component_cell = division_component_cell
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# 完成分裂后重新生成染色体组
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generation_ = np.array(range(len(component_cell)))
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pop_generation = []
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for _ in range(population_size):
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np.random.shuffle(generation_)
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pop_generation.append(generation_.tolist())
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else:
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break
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assert(len(best_component_cell) == len(best_population))
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return convert_cell_2_result(pcb_data, component_data, best_component_cell, best_population)
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