【TWVRP】基于matlab蚁群算法求解带时间窗的多中心车辆路径规划问题【含Matlab源码 113期】

欢迎来到海神之光博客之家✅博主简介热爱科研的Matlab仿真开发者修心和技术同步精进个人主页海神之光代码获取方式海神之光Matlab王者学习之路—代码获取方式⛳️座右铭行百里者半于九十。更多Matlab路径规划仿真内容点击①Matlab路径规划进阶版②付费专栏Matlab路径规划初级版⛳️关注CSDN海神之光更多资源等你来⛄一、VRP简介1 VRP基本原理车辆路径规划问题(Vehicle Routing ProblemVRP)是运筹学里重要的研究问题之一。VRP关注有一个供货商与K个销售点的路径规划的情况可以简述为对一系列发货点和收货点组织调用一定的车辆安排适当的行车路线使车辆有序地通过它们在满足指定的约束条件下例如货物的需求量与发货量交发货时间车辆容量限制行驶里程限制行驶时间限制等力争实现一定的目标如车辆空驶总里程最短运输总费用最低车辆按一定时间到达使用的车辆数最小等。VRP的图例如下所示2 问题属性与常见问题车辆路径问题的特性比较复杂总的来说包含四个方面的属性1地址特性包括车场数目、需求类型、作业要求。2车辆特性包括车辆数量、载重量约束、可运载品种约束、运行路线约束、工作时间约束。3问题的其他特性。4目标函数可能是总成本极小化或者极小化最大作业成本或者最大化准时作业。3 常见问题有以下几类1旅行商问题2带容量约束的车辆路线问题(CVRP)该模型很难拓展到VRP的其他场景,并且不知道具体车辆的执行路径因此对其模型继续改进。3带时间窗的车辆路线问题由于VRP问题的持续发展考虑需求点对于车辆到达的时间有所要求之下在车辆途程问题之中加入时窗的限制便成为带时间窗车辆路径问题VRP with Time Windows, VRPTW。带时间窗车辆路径问题VRPTW是在VRP上加上了客户的被访问的时间窗约束。在VRPTW问题中除了行驶成本之外, 成本函数还要包括由于早到某个客户而引起的等待时间和客户需要的服务时间。在VRPTW中车辆除了要满足VRP问题的限制之外还必须要满足需求点的时窗限制而需求点的时窗限制可以分为两种一种是硬时窗Hard Time Window硬时窗要求车辆必须要在时窗内到达早到必须等待而迟到则拒收另一种是软时窗Soft Time Window不一定要在时窗内到达但是在时窗之外到达必须要处罚以处罚替代等待与拒收是软时窗与硬时窗最大的不同。模型2(参考2017 A generalized formulation for vehicle routing problems)该模型为2维决策变量4收集和分发问题5多车场车辆路线问题参考(2005 lim多车场车辆路径问题的遗传算法_邹彤, 1996 renaud)由于车辆是同质的这里的建模在变量中没有加入车辆的维度。6优先约束车辆路线问题7相容性约束车辆路线问题8随机需求车辆路线问题4 解决方案1数学解析法2人机交互法3先分组再排路线法4先排路线再分组法5节省或插入法6改善或交换法7数学规划近似法8启发式算法5 VRP与VRPTW对比⛄二、蚁群算法简介1 蚁群算法(ant colony algorithm,ACA)起源和发展历程Marco Dorigo等人在研究新型算法的过程中发现蚁群在寻找食物时通过分泌一种称为信息素的生物激素交流觅食信息从而能快速的找到目标于是在1991年在其博士论文中首次系统地提出一种基于蚂蚁种群的新型智能优化算法“蚂蚁系统Ant system,简称AS”后来提出者及许多研究者对该算法作了各种改进将其应用于更为广泛的领域如图着色问题、二次分配问题、工件排序问题、车辆路径问题、车间作业调度问题、网络路由问题、大规模集成电路设计等。近些年来M.Dorigo等人把蚂蚁算法进一步发展成一种通用的优化技术“蚁群优化Ant Colony Optimization,简称ACO”并将所有符合ACO框架的算法称为“蚁群优化算法ACO algorithm”。具体来说各个蚂蚁在没有事先告知食物在什么地方的前提下开始寻找食物。当一只找到食物以后它会向环境释放一种挥发性分泌物pheromone (称为信息素,该物质随着时间的推移会逐渐挥发消失信息素浓度的大小表征路径的远近)信息素能够让其他蚂蚁感知从而起到一个引导的作用。通常多个路径上均有信息素时蚂蚁会优先选择信息素浓度高的路径从而使浓度高的路径信息素浓度更高形成一个正反馈。有些蚂蚁并没有像其它蚂蚁一样总重复同样的路他们会另辟蹊径如果另开辟的道路比原来的其他道路更短那么渐渐地更多的蚂蚁被吸引到这条较短的路上来。最后经过一段时间运行可能会出现一条最短的路径被大多数蚂蚁重复着。最终信息素浓度最高的路径即是最终被蚂蚁选中的最优路径。与其他算法相比蚁群算法是一种比较年轻的算法具有分布式计算、无中心控制、个体之间异步间接通信等特点并且易于与其他优化算法相结合经过不少仁人志士的不断探索到今天已经发展出了各式各样的改进蚁群算法不过蚁群算法的原理仍是主干。2 蚁群算法的求解原理基于上述对蚁群觅食行为的描述该算法主要对觅食行为进行以下几个方面模拟1模拟的图场景中包含了两种信息素一种表示家一种表示食物的地点并且这两种信息素都在以一定的速率进行挥发。2每个蚂蚁只能感知它周围的小部分地方的信息。蚂蚁在寻找食物的时候如果在感知范围内就可以直接过去如果不在感知范围内就要朝着信息素多的地方走蚂蚁可以有一个小概率不往信息素多的地方走而另辟蹊径这个小概率事件很重要代表了一种找路的创新对于找到更优的解很重要。3蚂蚁回窝的规则与找食物的规则相同。4蚂蚁在移动时候首先会根据信息素的指引如果没有信息素的指引会按照自己的移动方向惯性走下去但也有一定的机率改变方向蚂蚁还可以记住已经走过的路避免重复走一个地方。5蚂蚁在找到食物时留下的信息素最多然后距离食物越远的地方留下的信息素越少。找到窝的信息素留下的量的规则跟食物相同。蚁群算法有以下几个特点:正反馈算法、并发性算法、较强的鲁棒性、概率型全局搜索、不依赖严格的数学性质、搜索时间长易出现停止现象。蚂蚁转移概率公式公式中是蚂蚁k从城市i转移到j的概率αβ分别为信息素和启发式因子的相对重要程度为边ij上的信息素量为启发式因子为蚂蚁k下步允许选择的城市。上述公式即为蚂蚁系统中的信息素更新公式是边i,j)上的信息素量ρ是信息素蒸发系数0ρ1;为第k只蚂蚁在本次迭代中留在边i,j上的信息素量Q为一正常系数为第k只蚂蚁在本次周游中的路径长度。在蚂蚁系统中信息素更新公式为3 蚁群算法的求解步骤1初始化参数在计算之初需要对相关参数进行初始化如蚁群规模蚂蚁数量m、信息素重要程度因子α、启发函数重要程度因子β、信息素会发银子ρ、信息素释放总量Q、最大迭代次数iter_max、迭代次数初值iter1。2构建解空间将各个蚂蚁随机地置于不同的出发点对每个蚂蚁kk1,2,3…m按照2-1计算其下一个待访问城市直到所有蚂蚁访问完所有城市。3更新信息苏计算每个蚂蚁经过路径长度Lk(k1,2,…m记录当前迭代次数中的最优解最短路径。同时根据式2-2和2-3对各个城市连接路径上信息素浓度进行更新。4 判断是否终止若iteriter_max则令iteriter1,清空蚂蚁经过路径的记录表并返回步骤2否则终止计算输出最优解。5判断是否终止若iteriter_max则令iteriter1,清空蚂蚁经过路径的记录表并返回步骤2否则终止计算输出最优解。3. 判断是否终止若iteriter_max则令iteriter1,清空蚂蚁经过路径的记录表并返回步骤2否则终止计算输出最优解。⛄三、部分源代码function varargout AntColonyVRPGUI(varargin)% ANTCOLONYVRPGUI M-file for AntColonyVRPGUI.fig% ANTCOLONYVRPGUI, by itself, creates a new ANTCOLONYVRPGUI or raises the existing% singleton*.%% H ANTCOLONYVRPGUI returns the handle to a new ANTCOLONYVRPGUI or the handle to% the existing singleton*.%% ANTCOLONYVRPGUI(‘CALLBACK’,hObject,eventData,handles,…) calls the local% function named CALLBACK in ANTCOLONYVRPGUI.M with the given input arguments.%% ANTCOLONYVRPGUI(‘Property’,‘Value’,…) creates a new ANTCOLONYVRPGUI or raises the% existing singleton*. Starting from the left, property value pairs are% applied to the GUI before AntColonyVRPGUI_OpeningFcn gets called. An% unrecognized property name or invalid value makes property application% stop. All inputs are passed to AntColonyVRPGUI_OpeningFcn via varargin.%% *See GUI Options on GUIDE’s Tools menu. Choose “GUI allows only one% instance to run (singleton)”.%% See also: GUIDE, GUIDATA, GUIHANDLES% Edit the above text to modify the response to help AntColonyVRPGUI% Last Modified by GUIDE v2.5 11-Jun-2015 00:13:48% Begin initialization code - DO NOT EDITgui_Singleton 1;gui_State struct(‘gui_Name’, mfilename, …‘gui_Singleton’, gui_Singleton, …‘gui_OpeningFcn’, AntColonyVRPGUI_OpeningFcn, …‘gui_OutputFcn’, AntColonyVRPGUI_OutputFcn, …‘gui_LayoutFcn’, [] , …‘gui_Callback’, []);if nargin ischar(varargin{1})gui_State.gui_Callback str2func(varargin{1});endif nargout[varargout{1:nargout}] gui_mainfcn(gui_State, varargin{:});elsegui_mainfcn(gui_State, varargin{:});end% End initialization code - DO NOT EDIT% — Executes just before AntColonyVRPGUI is made visible.function AntColonyVRPGUI_OpeningFcn(hObject, eventdata, handles, varargin)% This function has no output args, see OutputFcn.% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% varargin command line arguments to AntColonyVRPGUI (see VARARGIN)% Choose default command line output for AntColonyVRPGUIhandles.output hObject;%% change !!!handles.ismdvrp true; %!!!%%if handles.ismdvrp true[handles.initClusters, handles.coordinates] INIT;handles.clusters handles.initClusters;set(handles.uipanelBases,Visible,on); create_init_plot_MDVRP(handles.coordinates, handles.clusters);else%initialization[vehicles,demands,dist_stations,dist_bases] INIT_VRP;handles.vehicles vehicles;handles.demands demands;handles.distances_stations dist_stations;handles.distances_bases dist_bases;set(handles.uipanelBases,Visible,off);end% Update handles structureguidata(hObject, handles);% UIWAIT makes AntColonyVRPGUI wait for user response (see UIRESUME)% uiwait(handles.figure1);% — Outputs from this function are returned to the command line.function varargout AntColonyVRPGUI_OutputFcn(hObject, eventdata, handles)% varargout cell array for returning output args (see VARARGOUT);% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Get default command line output from handles structurevarargout{1} handles.output;% — Executes on selection change in algorithmPopupmenu.function algorithmPopupmenu_Callback(hObject, eventdata, handles)% hObject handle to algorithmPopupmenu (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Hints: contents cellstr(get(hObject,‘String’)) returns algorithmPopupmenu contents as cell array% contents{get(hObject,‘Value’)} returns selected item from algorithmPopupmenu%set invisible error messagesset(handles.errorMandatoryEditFields,‘Visible’,‘off’);%erase text in edit fieldsset(handles.evaporationPheromoneEdit,‘String’,‘’);set(handles.attractStationEdit,‘String’,‘’);set(handles.increasePheromoneEdit,‘String’,‘’);set(handles.amountPheromoneEdit,‘String’,‘’);set(handles.eliteAntsEdit,‘String’,‘’);%set disabled all edit fieldsset(handles.evaporationPheromoneEdit,‘Enable’,‘off’);set(handles.attractStationEdit,‘Enable’,‘off’);set(handles.increasePheromoneEdit,‘Enable’,‘off’);set(handles.amountPheromoneEdit,‘Enable’,‘off’);set(handles.eliteAntsEdit,‘Enable’,‘off’);%set disabled run buttonset(handles.runButton,‘Enable’,‘off’);%set to 0 all resultsset(handles.lengthWayText,‘String’,‘-’);set(handles.subroutesNText,‘String’,‘-’);set(handles.timeSpentText,‘String’,‘-’);if handles.ismdvrp true%set invisible bases’ listboxset(handles.listboxBases,‘Visible’,‘off’);set(handles.totalLengthText,‘Visible’,‘off’);set(handles.totalLengthText,‘String’,‘-’);set(handles.totalLengthLabel,‘Visible’,‘off’);%clear plot cla; create_init_plot_MDVRP(handles.coordinates, handles.clusters);end%clear plot%cla;%clear legendlegend(‘off’);val get(hObject,‘Value’);if val ~ 1 %not placeholder’s text (not be executed)if val ~ 2 %not Clark-Wright algorithm%enable edit fieldsif val 5 %AntAlg with elite antsset(handles.eliteAntsEdit,‘Enable’,‘on’);endset(handles.evaporationPheromoneEdit,‘Enable’,‘on’);set(handles.attractStationEdit,‘Enable’,‘on’);set(handles.increasePheromoneEdit,‘Enable’,‘on’);set(handles.amountPheromoneEdit,‘Enable’,‘on’);end%enable run buttonset(handles.runButton,‘Enable’,‘on’);end% — Executes during object creation, after setting all properties.function algorithmPopupmenu_CreateFcn(hObject, eventdata, handles)% hObject handle to algorithmPopupmenu (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: popupmenu controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc isequal(get(hObject,‘BackgroundColor’), get(0,‘defaultUicontrolBackgroundColor’))set(hObject,‘BackgroundColor’,‘white’);endfunction evaporationPheromoneEdit_Callback(hObject, eventdata, handles)% hObject handle to evaporationPheromoneEdit (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,‘String’) returns contents of evaporationPheromoneEdit as text% str2double(get(hObject,‘String’)) returns contents of evaporationPheromoneEdit as a double% — Executes during object creation, after setting all properties.function evaporationPheromoneEdit_CreateFcn(hObject, eventdata, handles)% hObject handle to evaporationPheromoneEdit (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc isequal(get(hObject,‘BackgroundColor’), get(0,‘defaultUicontrolBackgroundColor’))set(hObject,‘BackgroundColor’,‘white’);endfunction attractStationEdit_Callback(hObject, eventdata, handles)% hObject handle to attractStationEdit (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,‘String’) returns contents of attractStationEdit as text% str2double(get(hObject,‘String’)) returns contents of attractStationEdit as a double% — Executes during object creation, after setting all properties.function attractStationEdit_CreateFcn(hObject, eventdata, handles)% hObject handle to attractStationEdit (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc isequal(get(hObject,‘BackgroundColor’), get(0,‘defaultUicontrolBackgroundColor’))set(hObject,‘BackgroundColor’,‘white’);endfunction increasePheromoneEdit_Callback(hObject, eventdata, handles)% hObject handle to increasePheromoneEdit (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,‘String’) returns contents of increasePheromoneEdit as text% str2double(get(hObject,‘String’)) returns contents of increasePheromoneEdit as a double% — Executes during object creation, after setting all properties.function increasePheromoneEdit_CreateFcn(hObject, eventdata, handles)% hObject handle to increasePheromoneEdit (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc isequal(get(hObject,‘BackgroundColor’), get(0,‘defaultUicontrolBackgroundColor’))set(hObject,‘BackgroundColor’,‘white’);endfunction amountPheromoneEdit_Callback(hObject, eventdata, handles)% hObject handle to amountPheromoneEdit (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,‘String’) returns contents of amountPheromoneEdit as text% str2double(get(hObject,‘String’)) returns contents of amountPheromoneEdit as a double% — Executes during object creation, after setting all properties.function amountPheromoneEdit_CreateFcn(hObject, eventdata, handles)% hObject handle to amountPheromoneEdit (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc isequal(get(hObject,‘BackgroundColor’), get(0,‘defaultUicontrolBackgroundColor’))set(hObject,‘BackgroundColor’,‘white’);endfunction eliteAntsEdit_Callback(hObject, eventdata, handles)% hObject handle to eliteAntsEdit (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,‘String’) returns contents of eliteAntsEdit as text% str2double(get(hObject,‘String’)) returns contents of eliteAntsEdit as a double% — Executes during object creation, after setting all properties.function eliteAntsEdit_CreateFcn(hObject, eventdata, handles)% hObject handle to eliteAntsEdit (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc isequal(get(hObject,‘BackgroundColor’), get(0,‘defaultUicontrolBackgroundColor’))set(hObject,‘BackgroundColor’,‘white’);end% — Executes on button press in runButton.function runButton_Callback(hObject, eventdata, handles)% hObject handle to runButton (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)%clear legendlegend(‘off’);%clear plot%cla;%set invisible error messagesset(handles.errorMandatoryEditFields,‘Visible’,‘off’);indexAlg get(handles.algorithmPopupmenu,‘Value’);if indexAlg ~ 1 %not placeholder’s text (not be executed)if indexAlg ~ 2 %not Clark-Wright algorithmif indexAlg 5 %AntAlg with elite antsif isempty(get(handles.eliteAntsEdit,‘String’))set(handles.errorMandatoryEditFields,‘Visible’,‘on’);return;endendif ( isempty(get(handles.evaporationPheromoneEdit,‘String’)) || …isempty(get(handles.attractStationEdit,‘String’)) || …isempty(get(handles.increasePheromoneEdit,‘String’)) || …isempty(get(handles.amountPheromoneEdit,‘String’)) )set(handles.errorMandatoryEditFields,Visible,on); return; end end %set disabled run button set(handles.runButton,Enable,off); if handles.ismdvrp true clusters runAlgorithmMDVRP(indexAlg, handles); handles.clusters clusters; listboxfindall(gcf,tag,listboxBases); guidata(listbox,handles); %select first value in bases listbox set(handles.listboxBases,Value,1); listboxBases_Callback(listbox, eventdata, handles); else runAlgorithmVRP(indexAlg, handles); end %set enabled run button set(handles.runButton,Enable,on); %set enabled zoom tools set(handles.toolZoomIn,Enable,on); set(handles.toolZoomOut,Enable,on); %set enabled pan tool set(handles.toolPan,Enable,on); zoom reset;endfunction [clusters] runAlgorithmMDVRP(index, handles)if index ~ 1if index ~ 2e str2double(get(handles.evaporationPheromoneEdit,‘String’));alpha str2double(get(handles.attractStationEdit,‘String’));beta str2double(get(handles.increasePheromoneEdit,‘String’));tau0 str2double(get(handles.amountPheromoneEdit,‘String’));E str2double(get(handles.eliteAntsEdit,‘String’));endtry tStart tic; %start spent time clusters handles.initClusters; handles.clusters clusters; basesName cell(1,length(clusters)1); basesName{1} Full view; for c 1:length(clusters) switch index case 2 %Clark-Wright [ Route, RouteLength, vehicles ] Clark_Wright_VRP( ... clusters(c).demands, clusters(c).diststations, ... clusters(c).distbases, clusters(c).vehicles_capacity ); case 3 %Ant-minpath [Route, RouteLength, vehicles] ANT_colony_algorithm_VRP_minpath( ... clusters(c).diststations, clusters(c).distbases, ... clusters(c).demands, [e alpha beta tau0], clusters(c).vehicles_capacity ); case 4 %Ant-partition [Route, RouteLength, vehicles] ANT_colony_algorithm_VRP( ... clusters(c).diststations, clusters(c).distbases, ... clusters(c).demands, [e alpha beta tau0], clusters(c).vehicles_capacity ); case 5 %Ant-elite ants [Route, RouteLength, vehicles] ANT_colony_algorithm_VRP_with_elite_ants( ... clusters(c).diststations, clusters(c).distbases, ... clusters(c).demands, [e alpha beta tau0 E], clusters(c).vehicles_capacity ); end handles.clusters(c).mdvrp changeStations(clusters, vehicles, RouteLength, Route, c); basesName{c 1} sprintf(Base #%d,c); %saving bases that need to be added to listbox end tElapsed toc(tStart); %end spent time catch ME msgbox(strcat(Error occured: ,ME.message),Error,error); end clusters handles.clusters; %clear plot cla; create_plot_route_with_vehicles_MDVRP(handles.coordinates, handles.clusters); set(handles.timeSpentText,String,sprintf(%fs,tElapsed)); set(handles.listboxBases, String, basesName); set(handles.listboxBases, Visible,on); set(handles.totalLengthText,String,num2str(getTotalLength(handles.clusters))); set(handles.totalLengthText,Visible,on); set(handles.totalLengthLabel,Visible,on); endfunction [mdvrp] changeStations(clusters, vehicles, LR, R, clusterN)mdvrp struct(‘length_route’,[],‘vehicles’,[],‘simpleVehicles’,[],‘num_of_subroutes’,[]);len length(vehicles);mdvrp.simpleVehicles vehicles;for v 1:lenvehicles(v).route(vehicles(v).route 1) 0;len_route length(vehicles(v).route);for vr 1:len_routeif vehicles(v).route(vr) ~ 0 %羼腓 礤 徉玎vehicles(v).route(vr) clusters(clusterN).stations(vehicles(v).route(vr)-1);endendendmdvrp.num_of_subroutes number_of_subroutes®;mdvrp.length_route LR;mdvrp.vehicles vehicles;function [tlength] getTotalLength(clusters)tlength 0;clen length(clusters);for c 1:clentlength tlength clusters©.mdvrp.length_route;endfunction runAlgorithmVRP(index, handles)if index ~ 1if index ~ 2e str2double(get(handles.evaporationPheromoneEdit,‘String’));alpha str2double(get(handles.attractStationEdit,‘String’));beta str2double(get(handles.increasePheromoneEdit,‘String’));tau0 str2double(get(handles.amountPheromoneEdit,‘String’));E str2double(get(handles.eliteAntsEdit,‘String’));endtry tStart tic; %start spent time switch index case 2 %Clark-Wright [Route,RouteLength,vehicles] Clark_Wright_VRP( handles.demands, ... handles.distances_stations, handles.distances_bases, handles.vehicles ); case 3 %Ant-minpath [Route,RouteLength,vehicles] ANT_colony_algorithm_VRP_minpath( handles.distances_stations,... handles.distances_bases, handles.demands, [e alpha beta tau0], handles.vehicles); case 4 %Ant-partition [Route,RouteLength,vehicles] ANT_colony_algorithm_VRP( handles.distances_stations, ... handles.distances_bases, handles.demands, [e alpha beta tau0], handles.vehicles); case 5 %Ant-elite ants [Route,RouteLength,vehicles] ANT_colony_algorithm_VRP_with_elite_ants( ... handles.distances_stations, handles.distances_bases, handles.demands, ... [e alpha beta tau0 E], handles.vehicles); end tElapsed toc(tStart); %end spent time create_plot_route_with_vehicles( add_bases_to_distances(handles.distances_stations, ... handles.distances_bases), vehicles, [0 handles.demands] ); catch ME msgbox(strcat(Error occured: ,ME.message),Error,error); end set(handles.lengthWayText,String,num2str(RouteLength)); set(handles.subroutesNText,String,num2str(number_of_subroutes(Route))); set(handles.timeSpentText,String,sprintf(%fs,tElapsed)); end% --------------------------------------------------------------------function dataTooltip_ClickedCallback(hObject, eventdata, handles)% hObject handle to dataTooltip (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% --------------------------------------------------------------------function algorithmTooltip_ClickedCallback(hObject, eventdata, handles)% hObject handle to algorithmTooltip (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% — Executes on selection change in listboxBases.function listboxBases_Callback(hObject, eventdata, handles)% hObject handle to listboxBases (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Hints: contents cellstr(get(hObject,‘String’)) returns listboxBases contents as cell array% contents{get(hObject,‘Value’)} returns selected item from listboxBases%clear legendlegend(‘off’);%clear plotcla;index get(handles.listboxBases,‘value’) - 1;if index 0 %if user want to see the full graph of routescreate_plot_route_with_vehicles_MDVRP(handles.coordinates, handles.clusters);set(handles.lengthWayText,‘String’,‘-’);set(handles.subroutesNText,‘String’,‘-’);elsecreate_plot_of_base_MDVRP(handles.coordinates, handles.clusters, index);set(handles.lengthWayText,‘String’,num2str(handles.clusters(index).mdvrp.length_route));set(handles.subroutesNText,‘String’,num2str(handles.clusters(index).mdvrp.num_of_subroutes));end% — Executes during object creation, after setting all properties.function listboxBases_CreateFcn(hObject, eventdata, handles)% hObject handle to listboxBases (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: listbox controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc isequal(get(hObject,‘BackgroundColor’), get(0,‘defaultUicontrolBackgroundColor’))set(hObject,‘BackgroundColor’,‘white’);end% --------------------------------------------------------------------function aboutMenu_Callback(hObject, eventdata, handles)% hObject handle to aboutMenu (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)About %call gui About.m% --------------------------------------------------------------------function fileMenu_Callback(hObject, eventdata, handles)% hObject handle to fileMenu (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% --------------------------------------------------------------------function anotherVRPSubmenu_Callback(hObject, eventdata, handles)% hObject handle to anotherVRPSubmenu (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% --------------------------------------------------------------------function newDataSubmenu_Callback(hObject, eventdata, handles)% hObject handle to newDataSubmenu (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)⛄四、运行结果⛄五、matlab版本及参考文献1 matlab版本2014a2 参考文献[1]雷金羡,孙宇,朱洪杰.改进蚁群算法在带时间窗车辆路径规划问题中的应用[J].计算机集成制造系统. 2022,28(11)3 备注简介此部分摘自互联网仅供参考若侵权联系删除 仿真咨询1 各类智能优化算法改进及应用生产调度、经济调度、装配线调度、充电优化、车间调度、发车优化、水库调度、三维装箱、物流选址、货位优化、公交排班优化、充电桩布局优化、车间布局优化、集装箱船配载优化、水泵组合优化、解医疗资源分配优化、设施布局优化、可视域基站和无人机选址优化2 机器学习和深度学习方面卷积神经网络CNN、LSTM、支持向量机SVM、最小二乘支持向量机LSSVM、极限学习机ELM、核极限学习机KELM、BP、RBF、宽度学习、DBN、RF、RBF、DELM、XGBOOST、TCN实现风电预测、光伏预测、电池寿命预测、辐射源识别、交通流预测、负荷预测、股价预测、PM2.5浓度预测、电池健康状态预测、水体光学参数反演、NLOS信号识别、地铁停车精准预测、变压器故障诊断3 图像处理方面图像识别、图像分割、图像检测、图像隐藏、图像配准、图像拼接、图像融合、图像增强、图像压缩感知4 路径规划方面旅行商问题TSP、车辆路径问题VRP、MVRP、CVRP、VRPTW等、无人机三维路径规划、无人机协同、无人机编队、机器人路径规划、栅格地图路径规划、多式联运运输问题、车辆协同无人机路径规划、天线线性阵列分布优化、车间布局优化5 无人机应用方面无人机路径规划、无人机控制、无人机编队、无人机协同、无人机任务分配6 无线传感器定位及布局方面传感器部署优化、通信协议优化、路由优化、目标定位优化、Dv-Hop定位优化、Leach协议优化、WSN覆盖优化、组播优化、RSSI定位优化7 信号处理方面信号识别、信号加密、信号去噪、信号增强、雷达信号处理、信号水印嵌入提取、肌电信号、脑电信号、信号配时优化8 电力系统方面微电网优化、无功优化、配电网重构、储能配置9 元胞自动机方面交通流 人群疏散 病毒扩散 晶体生长10 雷达方面卡尔曼滤波跟踪、航迹关联、航迹融合