整线优化第一版论文定稿工程

增加了整线批量测试
修改了现有min-max模型路径
修改了遗传算法整体框架
估计器增加异常数据剔除
封装优化结果类
修改供料器扫描算法中重复吸嘴组的判定

lineopt_genetic.py

@@ -203,39 +203,37 @@ def line_optimizer_genetic(component_data, machine_number):
     # population initialization
     population = selective_initialization(sorted(cp_points.items(), key=lambda x: x[0]), cp_feeders, population_size,
                                           machine_number)
+    # calculate fitness value
+    pop_val = [cal_individual_val(cp_points, cp_nozzle, machine_number, individual, estimator)[0] for individual in
+               population]
+
     with tqdm(total=n_generations) as pbar:
         pbar.set_description('genetic algorithm process for PCB assembly line balance')
 
         new_population = []
         for _ in range(n_generations):
-            # calculate fitness value
-            pop_val = []
-            for individual in population:
-                val, assigned_points = cal_individual_val(cp_points, cp_nozzle, machine_number, individual, estimator)
-                pop_val.append(val)
-
-            select_index = get_top_k_value(pop_val, population_size - len(new_population), reverse=False)
-            population = [population[idx] for idx in select_index]
-            pop_val = [pop_val[idx] for idx in select_index]
-
             population += new_population
             for individual in new_population:
                 val, _ = cal_individual_val(cp_points, cp_nozzle, machine_number, individual, estimator)
                 pop_val.append(val)
 
+            select_index = get_top_k_value(pop_val, population_size, reverse=False)
+            population = [population[idx] for idx in select_index]
+            pop_val = [pop_val[idx] for idx in select_index]
+
             # min-max convert
             max_val = max(pop_val)
-            pop_val = list(map(lambda v: max_val - v, pop_val))
-            sum_pop_val = sum(pop_val) + 1e-10
-            pop_val = [v / sum_pop_val + 1e-3 for v in pop_val]
+            sel_pop_val = list(map(lambda v: max_val - v, pop_val))
+            sum_pop_val = sum(sel_pop_val) + 1e-10
+            sel_pop_val = [v / sum_pop_val + 1e-3 for v in sel_pop_val]
 
             # crossover and mutation
             new_population = []
             for pop in range(population_size):
                 if pop % 2 == 0 and np.random.random() < crossover_rate:
-                    index1 = roulette_wheel_selection(pop_val)
+                    index1 = roulette_wheel_selection(sel_pop_val)
                     while True:
-                        index2 = roulette_wheel_selection(pop_val)
+                        index2 = roulette_wheel_selection(sel_pop_val)
                         if index1 != index2:
                             break