优化这段代码:def calDistanceMatrix(model): for i in range(len(model.demand_id_list)): from_node_id = model.demand_id_list[i] for j in range(i + 1, len(model.demand_id_list)): to_node_id = model.demand_id_list[j] dist = math.sqrt((model.demand_dict[from_node_id].x_coord - model.demand_dict[to_node_id].x_coord) ** 2 + (model.demand_dict[from_node_id].y_coord - model.demand_dict[to_node_id].y_coord) ** 2) model.distance_matrix[from_node_id, to_node_id] = dist model.distance_matrix[to_node_id, from_node_id] = dist for _, vehicle in model.vehicle_dict.items(): dist = math.sqrt((model.demand_dict[from_node_id].x_coord - vehicle.x_coord) ** 2 + (model.demand_dict[from_node_id].y_coord - vehicle.y_coord) ** 2) model.distance_matrix[from_node_id, vehicle.type] = dist model.distance_matrix[vehicle.type, from_node_id] = dist

时间: 2024-03-24 14:40:15 浏览: 20
可以优化的地方有两个: 1. 可以使用numpy中的ndarray来计算两点之间的距离,这样可以避免使用循环,提高计算速度。 2. 在计算距离矩阵之前,可以将所有点的坐标都存储到一个矩阵中,这样可以避免在循环中重复计算坐标。 优化后的代码如下所示: ```python import numpy as np def calDistanceMatrix(model): coord_matrix = np.array([(demand.x_coord, demand.y_coord) for demand in model.demand_dict.values()] + [(vehicle.x_coord, vehicle.y_coord) for vehicle in model.vehicle_dict.values()]) for i in range(len(model.demand_id_list)): from_node_id = model.demand_id_list[i] from_coord = coord_matrix[i] for j in range(i + 1, len(model.demand_id_list)): to_node_id = model.demand_id_list[j] to_coord = coord_matrix[j] dist = np.sqrt(np.sum((from_coord - to_coord) ** 2)) model.distance_matrix[from_node_id, to_node_id] = dist model.distance_matrix[to_node_id, from_node_id] = dist for _, vehicle in model.vehicle_dict.items(): vehicle_coord = coord_matrix[len(model.demand_dict) + vehicle.type] dist = np.sqrt(np.sum((from_coord - vehicle_coord) ** 2)) model.distance_matrix[from_node_id, vehicle.type] = dist model.distance_matrix[vehicle.type, from_node_id] = dist ``` 这样,代码的计算速度可以得到一定的提升。

相关推荐

优化代码:def crossSol(model): sol_list=copy.deepcopy(model.sol_list) model.sol_list=[] while True: f1_index = random.randint(0, len(sol_list) - 1) f2_index = random.randint(0, len(sol_list) - 1) if f1_index!=f2_index: f1 = copy.deepcopy(sol_list[f1_index]) f2 = copy.deepcopy(sol_list[f2_index]) if random.random() <= model.pc: cro1_index=int(random.randint(0,len(model.demand_id_list)-1)) cro2_index=int(random.randint(cro1_index,len(model.demand_id_list)-1)) new_c1_f = [] new_c1_m=f1.node_id_list[cro1_index:cro2_index+1] new_c1_b = [] new_c2_f = [] new_c2_m=f2.node_id_list[cro1_index:cro2_index+1] new_c2_b = [] for index in range(len(model.demand_id_list)):#遍历长度 if len(new_c1_f)<cro1_index: if f2.node_id_list[index] not in new_c1_m: new_c1_f.append(f2.node_id_list[index]) else: if f2.node_id_list[index] not in new_c1_m: new_c1_b.append(f2.node_id_list[index]) for index in range(len(model.demand_id_list)): if len(new_c2_f)<cro1_index: if f1.node_id_list[index] not in new_c2_m: new_c2_f.append(f1.node_id_list[index]) else: if f1.node_id_list[index] not in new_c2_m: new_c2_b.append(f1.node_id_list[index]) new_c1=copy.deepcopy(new_c1_f) new_c1.extend(new_c1_m) new_c1.extend(new_c1_b) f1.nodes_seq=new_c1 new_c2=copy.deepcopy(new_c2_f) new_c2.extend(new_c2_m) new_c2.extend(new_c2_b) f2.nodes_seq=new_c2 model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) else: model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) if len(model.sol_list)>model.popsize: break

优化这段代码:降低这段代码重复率:def crossSol(model): sol_list=copy.deepcopy(model.sol_list) model.sol_list=[] while True: f1_index = random.randint(0, len(sol_list) - 1) f2_index = random.randint(0, len(sol_list) - 1) if f1_index!=f2_index: f1 = copy.deepcopy(sol_list[f1_index]) f2 = copy.deepcopy(sol_list[f2_index]) if random.random() <= model.pc: cro1_index=int(random.randint(0,len(model.demand_id_list)-1)) cro2_index=int(random.randint(cro1_index,len(model.demand_id_list)-1)) new_c1_f = [] new_c1_m=f1.node_id_list[cro1_index:cro2_index+1] new_c1_b = [] new_c2_f = [] new_c2_m=f2.node_id_list[cro1_index:cro2_index+1] new_c2_b = [] for index in range(len(model.demand_id_list)): if len(new_c1_f)<cro1_index: if f2.node_id_list[index] not in new_c1_m: new_c1_f.append(f2.node_id_list[index]) else: if f2.node_id_list[index] not in new_c1_m: new_c1_b.append(f2.node_id_list[index]) for index in range(len(model.demand_id_list)): if len(new_c2_f)<cro1_index: if f1.node_id_list[index] not in new_c2_m: new_c2_f.append(f1.node_id_list[index]) else: if f1.node_id_list[index] not in new_c2_m: new_c2_b.append(f1.node_id_list[index]) new_c1=copy.deepcopy(new_c1_f) new_c1.extend(new_c1_m) new_c1.extend(new_c1_b) f1.nodes_seq=new_c1 new_c2=copy.deepcopy(new_c2_f) new_c2.extend(new_c2_m) new_c2.extend(new_c2_b) f2.nodes_seq=new_c2 model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) else: model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) if len(model.sol_list)>model.popsize: break

降低这段代码重复率:def crossSol(model): sol_list=copy.deepcopy(model.sol_list) model.sol_list=[] while True: f1_index = random.randint(0, len(sol_list) - 1) f2_index = random.randint(0, len(sol_list) - 1) if f1_index!=f2_index: f1 = copy.deepcopy(sol_list[f1_index]) f2 = copy.deepcopy(sol_list[f2_index]) if random.random() <= model.pc: cro1_index=int(random.randint(0,len(model.demand_id_list)-1)) cro2_index=int(random.randint(cro1_index,len(model.demand_id_list)-1)) new_c1_f = [] new_c1_m=f1.node_id_list[cro1_index:cro2_index+1] new_c1_b = [] new_c2_f = [] new_c2_m=f2.node_id_list[cro1_index:cro2_index+1] new_c2_b = [] for index in range(len(model.demand_id_list)): if len(new_c1_f)<cro1_index: if f2.node_id_list[index] not in new_c1_m: new_c1_f.append(f2.node_id_list[index]) else: if f2.node_id_list[index] not in new_c1_m: new_c1_b.append(f2.node_id_list[index]) for index in range(len(model.demand_id_list)): if len(new_c2_f)<cro1_index: if f1.node_id_list[index] not in new_c2_m: new_c2_f.append(f1.node_id_list[index]) else: if f1.node_id_list[index] not in new_c2_m: new_c2_b.append(f1.node_id_list[index]) new_c1=copy.deepcopy(new_c1_f) new_c1.extend(new_c1_m) new_c1.extend(new_c1_b) f1.nodes_seq=new_c1 new_c2=copy.deepcopy(new_c2_f) new_c2.extend(new_c2_m) new_c2.extend(new_c2_b) f2.nodes_seq=new_c2 model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) else: model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) if len(model.sol_list)>model.popsize: break

给我这段代码的伪代码:def crossSol(model): sol_list=copy.deepcopy(model.sol_list) model.sol_list=[] while True: f1_index = random.randint(0, len(sol_list) - 1) f2_index = random.randint(0, len(sol_list) - 1) if f1_index!=f2_index: f1 = copy.deepcopy(sol_list[f1_index]) f2 = copy.deepcopy(sol_list[f2_index]) if random.random() <= model.pc: cro1_index=int(random.randint(0,len(model.demand_id_list)-1)) cro2_index=int(random.randint(cro1_index,len(model.demand_id_list)-1)) new_c1_f = [] new_c1_m=f1.node_id_list[cro1_index:cro2_index+1] new_c1_b = [] new_c2_f = [] new_c2_m=f2.node_id_list[cro1_index:cro2_index+1] new_c2_b = [] for index in range(len(model.demand_id_list)): if len(new_c1_f)<cro1_index: if f2.node_id_list[index] not in new_c1_m: new_c1_f.append(f2.node_id_list[index]) else: if f2.node_id_list[index] not in new_c1_m: new_c1_b.append(f2.node_id_list[index]) for index in range(len(model.demand_id_list)): if len(new_c2_f)<cro1_index: if f1.node_id_list[index] not in new_c2_m: new_c2_f.append(f1.node_id_list[index]) else: if f1.node_id_list[index] not in new_c2_m: new_c2_b.append(f1.node_id_list[index]) new_c1=copy.deepcopy(new_c1_f) new_c1.extend(new_c1_m) new_c1.extend(new_c1_b) f1.nodes_seq=new_c1 new_c2=copy.deepcopy(new_c2_f) new_c2.extend(new_c2_m) new_c2.extend(new_c2_b) f2.nodes_seq=new_c2 model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) else: model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) if len(model.sol_list)>model.popsize: break

降低这段代码的重复率:#交叉 def crossSol(model): sol_list=copy.deepcopy(model.sol_list) model.sol_list=[] while True: f1_index = random.randint(0, len(sol_list) - 1) f2_index = random.randint(0, len(sol_list) - 1) if f1_index!=f2_index: f1 = copy.deepcopy(sol_list[f1_index]) f2 = copy.deepcopy(sol_list[f2_index]) if random.random() <= model.pc: cro1_index=int(random.randint(0,len(model.demand_id_list)-1)) cro2_index=int(random.randint(cro1_index,len(model.demand_id_list)-1)) new_c1_f = [] new_c1_m=f1.node_id_list[cro1_index:cro2_index+1] new_c1_b = [] new_c2_f = [] new_c2_m=f2.node_id_list[cro1_index:cro2_index+1] new_c2_b = [] for index in range(len(model.demand_id_list)): if len(new_c1_f)<cro1_index: if f2.node_id_list[index] not in new_c1_m: new_c1_f.append(f2.node_id_list[index]) else: if f2.node_id_list[index] not in new_c1_m: new_c1_b.append(f2.node_id_list[index]) for index in range(len(model.demand_id_list)): if len(new_c2_f)<cro1_index: if f1.node_id_list[index] not in new_c2_m: new_c2_f.append(f1.node_id_list[index]) else: if f1.node_id_list[index] not in new_c2_m: new_c2_b.append(f1.node_id_list[index]) new_c1=copy.deepcopy(new_c1_f) new_c1.extend(new_c1_m) new_c1.extend(new_c1_b) f1.nodes_seq=new_c1 new_c2=copy.deepcopy(new_c2_f) new_c2.extend(new_c2_m) new_c2.extend(new_c2_b) f2.nodes_seq=new_c2 model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) else: model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) if len(model.sol_list)>model.popsize: break

帮我翻译这段代码:#交叉 def crossSol(model): sol_list=copy.deepcopy(model.sol_list) model.sol_list=[] while True: f1_index = random.randint(0, len(sol_list) - 1) f2_index = random.randint(0, len(sol_list) - 1) if f1_index!=f2_index: f1 = copy.deepcopy(sol_list[f1_index]) f2 = copy.deepcopy(sol_list[f2_index]) if random.random() <= model.pc: cro1_index=int(random.randint(0,len(model.demand_id_list)-1)) cro2_index=int(random.randint(cro1_index,len(model.demand_id_list)-1)) new_c1_f = [] new_c1_m=f1.node_id_list[cro1_index:cro2_index+1] new_c1_b = [] new_c2_f = [] new_c2_m=f2.node_id_list[cro1_index:cro2_index+1] new_c2_b = [] for index in range(len(model.demand_id_list)): if len(new_c1_f)<cro1_index: if f2.node_id_list[index] not in new_c1_m: new_c1_f.append(f2.node_id_list[index]) else: if f2.node_id_list[index] not in new_c1_m: new_c1_b.append(f2.node_id_list[index]) for index in range(len(model.demand_id_list)): if len(new_c2_f)<cro1_index: if f1.node_id_list[index] not in new_c2_m: new_c2_f.append(f1.node_id_list[index]) else: if f1.node_id_list[index] not in new_c2_m: new_c2_b.append(f1.node_id_list[index]) new_c1=copy.deepcopy(new_c1_f) new_c1.extend(new_c1_m) new_c1.extend(new_c1_b) f1.nodes_seq=new_c1 new_c2=copy.deepcopy(new_c2_f) new_c2.extend(new_c2_m) new_c2.extend(new_c2_b) f2.nodes_seq=new_c2 model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) else: model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) if len(model.sol_list)>model.popsize: break

优化这段代码:def calTravelCost(route_list,model): timetable_list=[] distance_of_routes=0 time_of_routes=0 obj=0 for route in route_list: timetable=[] vehicle=model.vehicle_dict[route[0]] travel_distance=0 travel_time=0 v_type = route[0] free_speed=vehicle.free_speed fixed_cost=vehicle.fixed_cost variable_cost=vehicle.variable_cost for i in range(len(route)): if i == 0: next_node_id=route[i+1] travel_time_between_nodes=model.distance_matrix[v_type,next_node_id]/free_speed departure=max(0,model.demand_dict[next_node_id].start_time-travel_time_between_nodes) timetable.append((int(departure),int(departure))) elif 1<= i <= len(route)-2: last_node_id=route[i-1] current_node_id=route[i] current_node = model.demand_dict[current_node_id] travel_time_between_nodes=model.distance_matrix[last_node_id,current_node_id]/free_speed arrival=max(timetable[-1][1]+travel_time_between_nodes,current_node.start_time) departure=arrival+current_node.service_time timetable.append((int(arrival),int(departure))) travel_distance += model.distance_matrix[last_node_id, current_node_id] travel_time += model.distance_matrix[last_node_id, current_node_id]/free_speed+\ + max(current_node.start_time - arrival, 0) else: last_node_id = route[i - 1] travel_time_between_nodes = model.distance_matrix[last_node_id,v_type]/free_speed departure = timetable[-1][1]+travel_time_between_nodes timetable.append((int(departure),int(departure))) travel_distance += model.distance_matrix[last_node_id,v_type] travel_time += model.distance_matrix[last_node_id,v_type]/free_speed distance_of_routes+=travel_distance time_of_routes+=travel_time if model.opt_type==0: obj+=fixed_cost+travel_distance*variable_cost else: obj += fixed_cost + travel_time *variable_cost timetable_list.append(timetable) return timetable_list,time_of_routes,distance_of_routes,obj

降低这段代码的重复率:def calTravelCost(route_list,model): timetable_list=[] distance_of_routes=0 time_of_routes=0 obj=0 for route in route_list: timetable=[] vehicle=model.vehicle_dict[route[0]] travel_distance=0 travel_time=0 v_type = route[0] free_speed=vehicle.free_speed fixed_cost=vehicle.fixed_cost variable_cost=vehicle.variable_cost for i in range(len(route)): if i == 0: next_node_id=route[i+1] travel_time_between_nodes=model.distance_matrix[v_type,next_node_id]/free_speed departure=max(0,model.demand_dict[next_node_id].start_time-travel_time_between_nodes) timetable.append((int(departure),int(departure))) elif 1<= i <= len(route)-2: last_node_id=route[i-1] current_node_id=route[i] current_node = model.demand_dict[current_node_id] travel_time_between_nodes=model.distance_matrix[last_node_id,current_node_id]/free_speed arrival=max(timetable[-1][1]+travel_time_between_nodes,current_node.start_time) departure=arrival+current_node.service_time timetable.append((int(arrival),int(departure))) travel_distance += model.distance_matrix[last_node_id, current_node_id] travel_time += model.distance_matrix[last_node_id, current_node_id]/free_speed+\ + max(current_node.start_time - arrival, 0) else: last_node_id = route[i - 1] travel_time_between_nodes = model.distance_matrix[last_node_id,v_type]/free_speed departure = timetable[-1][1]+travel_time_between_nodes timetable.append((int(departure),int(departure))) travel_distance += model.distance_matrix[last_node_id,v_type] travel_time += model.distance_matrix[last_node_id,v_type]/free_speed distance_of_routes+=travel_distance time_of_routes+=travel_time if model.opt_type==0: obj+=fixed_cost+travel_distance*variable_cost else: obj += fixed_cost + travel_time *variable_cost timetable_list.append(timetable) return timetable_list,time_of_routes,distance_of_routes,obj

帮我翻译代码:def splitRoutes(node_id_list,model):V={i:[] for i in model.demand_id_list} V[-1]=[[0]*(len(model.vehicle_type_list)+4)] V[-1][0][0]=1 V[-1][0][1]=1 number_of_lables=1 for i in range(model.number_of_demands): n_1=node_id_list[i] j=i load=0 distance={v_type:0 for v_type in model.vehicle_type_list} while True: n_2=node_id_list[j] load=load+model.demand_dict[n_2].demand stop = False for k,v_type in enumerate(model.vehicle_type_list): vehicle=model.vehicle_dict[v_type] if i == j: distance[v_type]=model.distance_matrix[v_type,n_1]+model.distance_matrix[n_1,v_type] else: n_3=node_id_list[j-1] distance[v_type]=distance[v_type]-model.distance_matrix[n_3,v_type]+model.distance_matrix[n_3,n_2]\ +model.distance_matrix[n_2,v_type] route=node_id_list[i:j+1] route.insert(0,v_type) route.append(v_type) "检查时间窗口。只有在满足时间窗口时才能生成新标签。否则,跳过“" if not checkTimeWindow(route,model,vehicle): continue for id,label in enumerate(V[i-1]): if load<=vehicle.capacity and label[k+4]<vehicle.numbers: stop=True if model.opt_type==0: cost=vehicle.fixed_cost+distance[v_type]vehicle.variable_cost else: cost=vehicle.fixed_cost+distance[v_type]/vehicle.free_speedvehicle.variable_cost W=copy.deepcopy(label) "set the previous label id " W[1]=V[i-1][id][0] "set the vehicle type" W[2]=v_type "update travel cost" W[3]=W[3]+cost "update the number of vehicles used" W[k+4]=W[k+4]+1 if checkResidualCapacity(node_id_list[j+1:],W,model): label_list,number_of_lables=updateNodeLabels(V[j],W,number_of_lables) V[j]=label_list j+=1 if j>=len(node_id_list) or stop==False: break if len(V[model.number_of_demands-1])>0: route_list=extractRoutes(V, node_id_list, model) return route_list else: print("由于容量不足,无法拆分节点id列表") return None

最新推荐

recommend-type

QT5开发及实例配套源代码.zip

QT5开发及实例配套[源代码],Qt是诺基亚公司的C++可视化开发平台,本书以Qt 5作为平台,每个章节在简单介绍开发环境的基础上,用一个小实例,介绍Qt 5应用程序开发各个方面,然后系统介绍Qt 5应用程序的开发技术,一般均通过实例介绍和讲解内容。最后通过三个大实例,系统介绍Qt 5综合应用开发。光盘中包含本书教学课件和书中所有实例源代码及其相关文件。通过学习本书,结合实例上机练习,一般能够在比较短的时间内掌握Qt 5应用技术。本书既可作为Qt 5的学习和参考用书,也可作为大学教材或Qt 5培训用书。
recommend-type

grpcio-1.46.3-cp37-cp37m-musllinux_1_1_i686.whl

Python库是一组预先编写的代码模块,旨在帮助开发者实现特定的编程任务,无需从零开始编写代码。这些库可以包括各种功能,如数学运算、文件操作、数据分析和网络编程等。Python社区提供了大量的第三方库,如NumPy、Pandas和Requests,极大地丰富了Python的应用领域,从数据科学到Web开发。Python库的丰富性是Python成为最受欢迎的编程语言之一的关键原因之一。这些库不仅为初学者提供了快速入门的途径,而且为经验丰富的开发者提供了强大的工具,以高效率、高质量地完成复杂任务。例如,Matplotlib和Seaborn库在数据可视化领域内非常受欢迎,它们提供了广泛的工具和技术,可以创建高度定制化的图表和图形,帮助数据科学家和分析师在数据探索和结果展示中更有效地传达信息。
recommend-type

大学生毕业答辨ppt免费模板【不要积分】下载可编辑可用(138).zip

大学生毕业答辨ppt免费模板【不要积分】下载可编辑可用(138).zip
recommend-type

Eclipse的C/C++自动补全插件org.eclipse.cdt.ui-7.3.100.202111091601

Eclipse的C/C++自动补全插件,制作参考:https://blog.csdn.net/kingfox/article/details/104121203?spm=1001.2101.3001.6650.1&utm_medium=distribute.pc_relevant.none-task-blog-2~default~BlogCommendFromBaidu~Rate-1-104121203-blog-117118786.235%5Ev43%5Epc_blog_bottom_relevance_base1&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2~default~BlogCommendFromBaidu~Rate-1-104121203-blog-117118786.235%5Ev43%5Epc_blog_bottom_relevance_base1&utm_relevant_index=2
recommend-type

大学生毕业答辨ppt免费模板【不要积分】下载可编辑可用(137).zip

大学生毕业答辨ppt免费模板【不要积分】下载可编辑可用(137).zip
recommend-type

zigbee-cluster-library-specification

最新的zigbee-cluster-library-specification说明文档。
recommend-type

管理建模和仿真的文件

管理Boualem Benatallah引用此版本:布阿利姆·贝纳塔拉。管理建模和仿真。约瑟夫-傅立叶大学-格勒诺布尔第一大学,1996年。法语。NNT:电话:00345357HAL ID:电话:00345357https://theses.hal.science/tel-003453572008年12月9日提交HAL是一个多学科的开放存取档案馆,用于存放和传播科学研究论文,无论它们是否被公开。论文可以来自法国或国外的教学和研究机构,也可以来自公共或私人研究中心。L’archive ouverte pluridisciplinaire
recommend-type

MATLAB柱状图在信号处理中的应用:可视化信号特征和频谱分析

![matlab画柱状图](https://img-blog.csdnimg.cn/3f32348f1c9c4481a6f5931993732f97.png) # 1. MATLAB柱状图概述** MATLAB柱状图是一种图形化工具,用于可视化数据中不同类别或组的分布情况。它通过绘制垂直条形来表示每个类别或组中的数据值。柱状图在信号处理中广泛用于可视化信号特征和进行频谱分析。 柱状图的优点在于其简单易懂,能够直观地展示数据分布。在信号处理中,柱状图可以帮助工程师识别信号中的模式、趋势和异常情况,从而为信号分析和处理提供有价值的见解。 # 2. 柱状图在信号处理中的应用 柱状图在信号处理
recommend-type

用Spring boot和vue写一个登录注册界面

好的,这是一个比较常见的技术问题,我可以为您提供一些参考资料和思路。首先,您可以使用Spring Boot作为后端框架来处理用户认证和注册的逻辑,比如使用Spring Security实现用户登录认证。同时,您还需要设计相应的数据模型和数据库表结构来存储用户信息。在前端方面,您可以使用Vue.js作为框架来构建登录注册页面,使用Axios来发起API请求并和后端进行交互。当然,在实现过程中,还需要考虑一些具体细节,比如数据校验、安全性和用户体验等方面。希望这些信息能够帮助到您。
recommend-type

JSBSim Reference Manual

JSBSim参考手册,其中包含JSBSim简介,JSBSim配置文件xml的编写语法,编程手册以及一些应用实例等。其中有部分内容还没有写完,估计有生之年很难看到完整版了,但是内容还是很有参考价值的。