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、TSP简介旅行商问题即TSP问题Traveling Salesman Problem又译为旅行推销员问题、货郎担问题是数学领域中著名问题之一。假设有一个旅行商人要拜访n个城市他必须选择所要走的路径路径的限制是每个城市只能拜访一次而且最后要回到原来出发的城市。路径的选择目标是要求得的路径路程为所有路径之中的最小值。TSP的数学模型⛄二、粒子群算法简介1 算法1.1 原理1.2 性能比较1.3 步骤⛄三、部分源代码function varargout PSO(varargin)% PSO M-file for PSO.fig% PSO, by itself, creates a new PSO or raises the existing% singleton*.%% H PSO returns the handle to a new PSO or the handle to% the existing singleton*.%% PSO(‘CALLBACK’,hObject,eventData,handles,…) calls the local% function named CALLBACK in PSO.M with the given input arguments.%% PSO(‘Property’,‘Value’,…) creates a new PSO or raises the% existing singleton*. Starting from the left, property value pairs are% applied to the GUI before PSO_OpeningFunction gets called. An% unrecognized property name or invalid value makes property application% stop. All inputs are passed to PSO_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 PSO% Last Modified by GUIDE v2.5 12-Jun-2009 22:11:08% Begin initialization code - DO NOT EDITgui_Singleton 1;gui_State struct(‘gui_Name’, mfilename, …‘gui_Singleton’, gui_Singleton, …‘gui_OpeningFcn’, PSO_OpeningFcn, …‘gui_OutputFcn’, PSO_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 PSO is made visible.function PSO_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 PSO (see VARARGIN)% Choose default command line output for PSOhandles.output hObject;% Update handles structureguidata(hObject, handles);% UIWAIT makes PSO wait for user response (see UIRESUME)% uiwait(handles.figure1);% — Outputs from this function are returned to the command line.function varargout PSO_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 button press in run.function run_Callback(hObject, eventdata, handles)% hObject handle to run (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)TSP_type get(findobj(‘tag’,‘tsp’),‘Value’);switch TSP_typecase 1dataload(‘burma14.txt’);case 2dataload(‘ulysses22.txt’);case 3dataload(‘bayg29.txt’);case 4dataload(‘Oliver30.txt’);case 5dataload(‘eil51.txt’);case 6dataload(‘st70.txt’);case 7dataload(‘pr76.txt’);case 8dataload(‘gr96.txt’);case 9dataload(‘ch130.txt’);case 10dataload(‘ch150.txt’);case 11dataload(‘pr226.txt’);endadata(:,2);bdata(:,3);C[a b]; %城市坐标矩阵nsize(C,1); %城市数目Dzeros(n,n); %城市距离矩阵%L_bestones(Nmax,1);for i1:nfor j1:nif i~jD(i,j)((C(i,1)-C(j,1))2(C(i,2)-C(j,2))2)^0.5;endD(j,i)D(i,j);endendNmaxstr2double(get(findobj(‘tag’,‘N_max’),‘string’));mstr2double(get(findobj(‘tag’,‘m’),‘string’));algo_type get(findobj(‘tag’,‘algo’),‘Value’);switch algo_typecase 1%% 初始化所有粒子for i1:mx(i,:)randperm(n); %粒子位置endFfitness(x,C,D); %计算种群适应度%xuhaoxulie(F) %最小适应度种群序号a1F(1);a21;for i1:mif a1F(i)a1F(i);a2i;endendxuhaoa2;Tour_pbestx; %当前个体最优Tour_gbestx(xuhao,:) ; %当前全局最优路径Pbinfones(1,m); %个体最优记录GbF(a2); %群体最优记录xnew1x;N1;while NNmax%计算适应度Ffitness(x,C,D);for i1:mif F(i)Pb(i)Pb(i)F(i); %将当前值赋给新的最佳值Tour_pbest(i,:)x(i,:);%将当前路径赋给个体最优路径endif F(i)GbGbF(i);Tour_gbestx(i,:);endend% numminxulie(Pb) %最小适应度种群序号a1Pb(1);a21;for i1:mif a1Pb(i)a1Pb(i);a2i;endendnummina2;Gb(N)Pb(nummin); %当前群体最优长度for i1:m%% 与个体最优进行交叉c1round(rand(n-2))1; %在[1,n-1]范围内随机产生一个交叉位c2round(rand*(n-2))1;while c1c2c1round(rand*(n-2))1; %在[1,n-1]范围内随机产生一个交叉位c2round(rand*(n-2))1;endchb1min(c1,c2);chb2max(c1,c2);crosTour_pbest(i,chb1:chb2); %交叉区域矩阵ncrossize(cros,2); %交叉区域元素个数%删除与交叉区域相同元素for j1:ncrosfor k1:nif xnew1(i,k)cros(j)xnew1(i,k)0;for t1:n-ktempxnew1(i,kt-1);xnew1(i,kt-1)xnew1(i,kt);xnew1(i,kt)temp;endendendendxnewxnew1;%插入交叉区域for j1:ncrosxnew1(i,n-ncrosj)cros(j);end%判断产生新路径长度是否变短dist0;for j1:n-1distdistD(xnew1(i,j),xnew1(i,j1));enddistdistD(xnew1(i,1),xnew1(i,n));if F(i)distx(i,:)xnew1(i,:);end%% 与全体最优进行交叉c1round(rand*(n-2))1; %在[1,n-1]范围内随机产生一个交叉位c2round(rand*(n-2))1;while c1c2c1round(rand*(n-2))1; %在[1,n-1]范围内随机产生一个交叉位c2round(rand*(n-2))1;endchb1min(c1,c2);chb2max(c1,c2);crosTour_gbest(chb1:chb2); %交叉区域矩阵ncrossize(cros,2); %交叉区域元素个数%删除与交叉区域相同元素for j1:ncrosfor k1:nif xnew1(i,k)cros(j)xnew1(i,k)0;for t1:n-ktempxnew1(i,kt-1);xnew1(i,kt-1)xnew1(i,kt);xnew1(i,kt)temp;endendendendxnewxnew1;%插入交叉区域for j1:ncrosxnew1(i,n-ncrosj)cros(j);end%判断产生新路径长度是否变短dist0;for j1:n-1distdistD(xnew1(i,j),xnew1(i,j1));enddistdistD(xnew1(i,1),xnew1(i,n));if F(i)distx(i,:)xnew1(i,:);end%% 进行变异操作c1round(rand*(n-1))1; %在[1,n]范围内随机产生一个变异位c2round(rand*(n-1))1;tempxnew1(i,c1);xnew1(i,c1)xnew1(i,c2);xnew1(i,c2)temp;%判断产生新路径长度是否变短dist0;for j1:n-1distdistD(xnew1(i,j),xnew1(i,j1));enddistdistD(xnew1(i,1),xnew1(i,n));%distdist(xnew1(i,:),D);if F(i)distx(i,:)xnew1(i,:);endend%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% F(x,C,D) %计算种群适应度%xuhaoxulie(F) %最小适应度种群序号a1F(1);a21;for i1:mif a1F(i)a1F(i);a2i;endendxuhaoa2;L_best(N)min(F);Tour_gbestx(xuhao,:); %当前全局最优路径NN1;axes(handles.city) %城市路径状态scatter(C(:,1),C(:,2));hold onplot([C(Tour_gbest(1),1),C(Tour_gbest(n),1)],[C(Tour_gbest(1),2),C(Tour_gbest(n),2)],‘ms-’,‘LineWidth’,2,‘MarkerEdgeColor’,‘k’,‘MarkerFaceColor’,‘g’)for ii2:nplot([C(Tour_gbest(ii-1),1),C(Tour_gbest(ii),1)],[C(Tour_gbest(ii-1),2),C(Tour_gbest(ii),2)],‘ms-’,‘LineWidth’,2,‘MarkerEdgeColor’,‘k’,‘MarkerFaceColor’,‘g’)endhold offaxes(handles.shoulian) %收敛曲线plot(L_best);set(findobj(‘tag’,‘N’),‘string’,num2str(N-1));%当前迭代次数set(findobj(‘tag’,‘tour’),‘string’,num2str(Tour_gbest));%当前最优路径set(findobj(‘tag’,‘L’),‘string’,num2str(min(L_best)));%当前最优路径长度 %%%这里的L_best是当前最优路径end⛄四、运行结果⛄五、matlab版本及参考文献1 matlab版本2014a2 参考文献[1] 包子阳,余继周,杨杉.智能优化算法及其MATLAB实例第2版[M].电子工业出版社2016.[2]张岩,吴水根.MATLAB优化算法源代码[M].清华大学出版社2017.3 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