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关于建模的一点点体会	数模经验分享	MX22221 2011-10-5	19 6182	1617886226 2013-5-19 22:57
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遗传算法代码主要是求旅行问题	Matlab 资料库视频教程讲义代码	xwppopping 2012-8-18	10 3964	lzp小飞侠 2016-4-18 09:19
VRP问题（多旅行商问题（MTSP））	全国大学生数学建模竞赛(CUMCM)	taowenbao 2012-9-1	47 19556	362011071 2018-11-20 16:26
VRP问题的lingo程序（多旅行商问题） - [!price! 5 点体力]	全国大学生数学建模竞赛(CUMCM)	taowenbao 2012-9-1	40 23349	我昨天 2020-6-17 22:21

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分享旅行销售员（Traveling Salesman Problem）MATLAB代码: willshine19 2012-9-1 15:22; %TSP_GA Traveling Salesman Problem (TSP) Genetic Algorithm (GA) % Finds a (near) optimal solution to the TSP by setting up a GA to search % for the shortest route (least distance for the salesman to travel to % each city exactly once and return to the starting city) % % Summary: % 1. A single salesman travels to each of the cities and completes the % route by returning to the city he started from % 2. Each city is visited by the salesman exactly once % % Input: % XY (float) is an Nx2 matrix of city locations, where N is the number of cities % DMAT (float) is an NxN matrix of point to point distances/costs % POPSIZE (scalar integer) is the size of the population (should be divisible by 4) % NUMITER (scalar integer) is the number of desired iterations for the algorithm to run % SHOWPROG (scalar logical) shows the GA progress if true % SHOWRESULT (scalar logical) shows the GA results if true % % Output: % OPTROUTE (integer array) is the best route found by the algorithm % MINDIST (scalar float) is the cost of the best route % % Example: % n = 50; % xy = 10*rand(n,2); % popSize = 60; % numIter = 1e4; % showProg = 1; % showResult = 1; % a = meshgrid(1:n); % dmat = reshape(sqrt(sum((xy(a,:)-xy(a',:)).^2,2)),n,n); % = tsp_ga(xy,dmat,popSize,numIter,showProg,showResult); % % Example: % n = 100; % phi = (sqrt(5)-1)/2; % theta = 2*pi*phi*(0:n-1); % rho = (1:n).^phi; % = pol2cart(theta(:),rho(:)); % xy = 10*( -min( ))/(max( )-min( )); % popSize = 60; % numIter = 2e4; % a = meshgrid(1:n); % dmat = reshape(sqrt(sum((xy(a,:)-xy(a',:)).^2,2)),n,n); % = tsp_ga(xy,dmat,popSize,numIter,1,1); % % Example: % n = 50; % xyz = 10*rand(n,3); % popSize = 60; % numIter = 1e4; % showProg = 1; % showResult = 1; % a = meshgrid(1:n); % dmat = reshape(sqrt(sum((xyz(a,:)-xyz(a',:)).^2,2)),n,n); % = tsp_ga(xyz,dmat,popSize,numIter,showProg,showResult); % % See also: mtsp_ga, tsp_nn, tspo_ga, tspof_ga, tspofs_ga, distmat % % Author: Joseph Kirk % Email: jdkirk630@gmail.com % Release: 2.3 % Release Date: 11/07/11 function varargout = tsp_ga(xy,dmat,popSize,numIter,showProg,showResult) % Process Inputs and Initialize Defaults nargs = 6; for k = nargin:nargs-1 switch k case 0 xy = 10*rand(50,2); case 1 N = size(xy,1); a = meshgrid(1:N); dmat = reshape(sqrt(sum((xy(a,:)-xy(a',:)).^2,2)),N,N); case 2 popSize = 100; case 3 numIter = 1e4; case 4 showProg = 1; case 5 showResult = 1; otherwise end end % Verify Inputs = size(xy); = size(dmat); if N ~= nr || N ~= nc error('Invalid XY or DMAT inputs!') end n = N; % Sanity Checks popSize = 4*ceil(popSize/4); numIter = max(1,round(real(numIter(1)))); showProg = logical(showProg(1)); showResult = logical(showResult(1)); % Initialize the Population pop = zeros(popSize,n); pop(1,:) = (1:n); for k = 2:popSize pop(k,:) = randperm(n); end % Run the GA globalMin = Inf; totalDist = zeros(1,popSize); distHistory = zeros(1,numIter); tmpPop = zeros(4,n); newPop = zeros(popSize,n); if showProg pfig = figure('Name','TSP_GA | Current Best Solution','Numbertitle','off'); end for iter = 1:numIter % Evaluate Each Population Member (Calculate Total Distance) for p = 1:popSize d = dmat(pop(p,n),pop(p,1)); % Closed Path for k = 2:n d = d + dmat(pop(p,k-1),pop(p,k)); end totalDist(p) = d; end % Find the Best Route in the Population = min(totalDist); distHistory(iter) = minDist; if minDist globalMin globalMin = minDist; optRoute = pop(index,:); if showProg % Plot the Best Route figure(pfig); rte = optRoute( ); if dims 2, plot3(xy(rte,1),xy(rte,2),xy(rte,3),'r.-'); else plot(xy(rte,1),xy(rte,2),'r.-'); end title(sprintf('Total Distance = %1.4f, Iteration = %d',minDist,iter)); end end % Genetic Algorithm Operators randomOrder = randperm(popSize); for p = 4:4:popSize rtes = pop(randomOrder(p-3:p),:); dists = totalDist(randomOrder(p-3:p)); = min(dists); %#ok bestOf4Route = rtes(idx,:); routeInsertionPoints = sort(ceil(n*rand(1,2))); I = routeInsertionPoints(1); J = routeInsertionPoints(2); for k = 1:4 % Mutate the Best to get Three New Routes tmpPop(k,:) = bestOf4Route; switch k case 2 % Flip tmpPop(k,I:J) = tmpPop(k,J:-1:I); case 3 % Swap tmpPop(k, ) = tmpPop(k, ); case 4 % Slide tmpPop(k,I:J) = tmpPop(k, ); otherwise % Do Nothing end end newPop(p-3:p,:) = tmpPop; end pop = newPop; end if showResult % Plots the GA Results figure('Name','TSP_GA | Results','Numbertitle','off'); subplot(2,2,1); pclr = ~get(0,'DefaultAxesColor'); if dims 2, plot3(xy(:,1),xy(:,2),xy(:,3),'.','Color',pclr); else plot(xy(:,1),xy(:,2),'.','Color',pclr); end title('City Locations'); subplot(2,2,2); imagesc(dmat(optRoute,optRoute)); title('Distance Matrix'); subplot(2,2,3); rte = optRoute( ); if dims 2, plot3(xy(rte,1),xy(rte,2),xy(rte,3),'r.-'); else plot(xy(rte,1),xy(rte,2),'r.-'); end title(sprintf('Total Distance = %1.4f',minDist)); subplot(2,2,4); plot(distHistory,'b','LineWidth',2); title('Best Solution History'); set(gca,'XLim', ,'YLim', )]); end % Return Outputs if nargout varargout{1} = optRoute; varargout{2} = minDist; end; 1007 次阅读|0 个评论

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