Path planning based on matlab ant Colony algorithm shortest path planning

% Ant main program
 
clear all;
close all;
clc;
 
tic;
Ant=25; % Number of ants Ger=120; % number of iterations first_address = [100.10
    150.10
    180.30
    200.10
    200.200
    200.220
    180.240
    180.270
    150.270
    100.240
    80.240
    50.270
    200.300
    10.300
    10.270
    10.240
    10.200
    10.10
    50.30
    100.10]; SumOfCity = size(first_address,1); % Number of nodes length_address =10000.*ones(SumOfCity,SumOfCity); % length_ADDRESS Specifies the initial distance between two nodes10000, infinity can be set to indicate that the length_address(1.2) =377; % indicates node1And the node2The distance from the length_address (2.4) =190;
length_address(2.3) =100;
length_address(3.4) =101;
length_address(4.5) =240;
length_address(5.17) =1932;
length_address(5.6) =70;
length_address(6.13) =200;
length_address(6.7) =63.1;
 
length_address(7.10) =377;
length_address(7.8) =87.5;
length_address(8.9) =100;
length_address(10.11) =8;
length_address(9.10) =170.8;
length_address(9.12) =332.9;
length_address(11.12) =168.8;
length_address(11.16) =375.2;
length_address(12.15) =135.1;
 
length_address(13.14) =458;
length_address(14.15) =100;
length_address(15.16) =86.7;
length_address(16.17) =187.5;
length_address(17.18) =639.8;
 
length_address(18.20) =510.5;
length_address(18.19) =200.1;
length_address(19.20) =246.8;
for   n=1:size(first_address)
    for m=1:size(first_address)
        if length_address(n,m)~=10000length_address(m,n)=length_address(n,m); End end power=length_address; % distance [PM PN]=size(power); % distance matrix size, number of rows %% % draw the node distribution graph % figure(1);
% grid on;
% hold on;
% scatter(first_address(:,1),first_address(:,2));
% for i=1:PN
%     for j=1:PN
%         if(length_address(i,j)~=10000)
%             line([first_address(i,1),first_address(j,1)],[first_address(i,2),first_address(j,2)].'Color'.'g'); % line % text((first_address(I,1)+first_address(j,1)) /2,(first_address(i,2)+first_address(j,2)) /2,num2str(length_address(i,j))); % Mark line segment distance % end % end % End % Initialize Ant location v=init_population(Ant,PN); v(:,1) =1; V (:, PN) = % starting point1; The finish fit = % short_road_fun (v, the power); % Find the distance between each path T0 = Max (fit)-fit; % Initialize vmFit =[]; vx=[]; P0=0.2; % P0---- Global transfer selection factor P=0.8; % P ---- pheromone evaporation coefficient %C=[]; % start searchfor i_ger=1:Ger
    lamda=1/i_ger; % transfer step parameter [T_Best(i_ger),BestIndex]= Max (T0);for j_g=1:Ant % to calculate the global transition probability r=T0(BestIndex) -t0 (j_g); Prob(i_ger,j_g)=r/T0(BestIndex); End % of100Only the ants make the switchfor j_g_tr=1The Ant % path is stored in the temp variable,1Represents the number of nodes that pass through the columnif Prob(i_ger,j_g_tr)<P0
            M=rand(1,PN)<lamda;
            temp=v(j_g_tr,:)- 2.*(v(j_g_tr,:).*M)+M;
        else
            M=rand(1,PN)<P0;
            temp=v(j_g_tr,:)- 2.*(v(j_g_tr,:).*M)+M;
        end
        temp(:,1)=1; % Ants must exist temp(:,end)=1; % Determine whether the temporary path distance after transformation is smaller than the original path. If yes, convert the ant path to the path stored in tempif short_road_fun(temp,power)<short_road_fun(v(j_g_tr,:),power)
            v(j_g_tr,:)=temp; End End % Pheromone update [sol,indb]=min(fit); v(1,:)=v(indb,:); % First ant path save minimum path media=mean(fit); vx=[vx sol]; % vmfit=[vmfit media]; End % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % final result shows that the optimal solution and optimal value % v (indb, disp ()sprintf('Code of shortroad is: %s',num2str(v(indb,:))));
disp(sprintf('\n')); % Empty line disp(sprintf('Shortroad is: %s',num2str(find(v(indb,:)))));
disp(sprintf('Mininum is: %d',sol)); route=find(v(indb,:)); % Draw the node distribution figure(2);
grid on;
hold on;
for i=1:PN- 1
    plot(first_address(i,1),first_address(i,2),'bo'.'MarkerSize'.10);
    str=num2str(i);
    text(first_address(i,1)- 10,first_address(i,2) +10,str,'Color'.'red'.'FontSize'.15);
end
m=length(route);
for i=1:m
    plot(first_address(route(i),1),first_address(route(i),2),'MarkerSize'.10.'MarkerEdgeColor'.'k'.'MarkerFaceColor'[0.5.0.5.0.5]); hold on; endfor i=1:PN
    for j=1:PN
        if(length_address(i,j)~=10000)
            line([first_address(i,1),first_address(j,1)],[first_address(i,2),first_address(j,2)].'Color'.'g'.'LineWidth'.5); % underlined text ((first_address (I,1)+first_address(j,1)) /2,(first_address(i,2)+first_address(j,2)) /2,num2str(length_address(i,j))); End End end %% Indicates the shortest pathfor p=1:m- 1
    if(route(p+1) ~ =20)
        line([first_address(route(p),1),first_address(route(p+1),1)],[first_address(route(p),2),first_address(route(p+1),2)].'Color'.'r'.'LineWidth'.5); % underlined text ((first_address (route (p),1)+first_address(route(p+1),1)) /2,(first_address(route(p),2)+first_address(route(p+1),2)) /2,num2str(length_address(route(p),route(p+1)))); % marks line segment distanceelse
        line([first_address(route(p),1),first_address(1.1)],[first_address(route(p),2),first_address(1.2)].'Color'.'r'.'LineWidth'.5); % underlined text ((first_address (route (p),1)+first_address(1.1)) /2,(first_address(route(p),2)+first_address(1.2)) /2,num2str(length_address(route(p),route(p+1)))); % marks line segment distanceend
end
axis([0 250 0 400])% Graph shows the trend % of optimal and average function valuesfigure(3);
% plot(vx);
% title('Optimal, trend of average function value');
% xlabel('Generations');
% ylabel('f(x)');
% hold on;
% plot(vmfit,'r');
% hold off;
 
runtime=toc
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Version: 2014 a