A list,

1 Origin and development history of Ant Colony Algorithm (ACA) Marco Dorigo et al. found that ant colonies can quickly find targets by secreting biohormones called pheromones to communicate foraging information when searching for food. Therefore, in his doctoral dissertation in 1991, he first systematically proposed a new intelligent optimization algorithm “Ant System” (AS for short) based on Ant population. Later, the proposer and many researchers made various improvements to the algorithm and applied it to a wider range of fields. Figure coloring problem, secondary assignment problem, workpiece sequencing problem, vehicle routing problem, job shop scheduling problem, network routing problem, large-scale integrated circuit design and so on. In recent years, M.Dorigo et al. further developed Ant algorithm into a general Optimization technology “Ant Colony Optimization (ACO)”, and called all the algorithms conforming to ACO framework “ACO algorithm”.



Specifically, individual ants start looking for food without first telling them where it is. When an ant finds food, it releases a volatile pheromone (called a pheromone, it evaporates over time and its concentration indicates how far the path is) into the environment that other ants sense as a guide. Generally, when there are pheromones on multiple paths, the ant will preferentially choose the path with high pheromone concentration, so that the pheromone concentration of the path with high pheromone concentration will be higher, forming a positive feedback. Some ants do not repeat the same route as others. They take a different route. If the alternative path is shorter than the original one, gradually more ants are attracted to the shorter path. Finally, after some time of running, there may be a shortest path repeated by most ants. In the end, the path with the highest pheromone concentration is the optimal one selected by the ant.

Compared with other algorithms, ant colony algorithm is a relatively young algorithm, characteristics, such as distributed computing center control and asynchronous indirect communication between individuals, and is easy to be combined with other optimization algorithms, through many healthyenterprise continuously explore, to today has developed a variety of improved ant colony algorithm, but the principle of ant colony algorithm is still the main.

2. Solving principle of ant colony algorithm

Based on the above description of ant colony foraging behavior, the algorithm mainly simulates foraging behavior from the following aspects:

(1) The simulated graph scene contains two pheromones, one representing home and one representing food location, and both pheromones are volatilized at a certain rate.

(2) Each ant can perceive information only in a small part of the area around it. Ants searching for food, if within the scope of the perception, can directly in the past, if is beyond the scope of awareness, will be toward more than pheromones, ants can have a small probability pheromone many places don’t go, and instead, it is very important to the small probability event, represents a way of innovation, is very important to find a better solution.

(3) Ants return to the nest using the same rules as when they find food.

(4) When ants move, they will first follow the guidance of pheromone. If there is no guidance of pheromone, they will follow the direction of their moving inertia, but there is a certain probability of changing direction. Ants can also remember the path they have already walked, so as to avoid repeating the same place.

(5) The ants leave the most pheromones when they find food, and then the farther away they are from the food, the less pheromone they leave. The rules for finding nest pheromones are the same as for food. Ant colony algorithm has the following characteristics: positive feedback algorithm, concurrency algorithm, strong robustness, probabilistic global search, does not rely on strict mathematical properties, long search time, easy to stop phenomenon.

Ant transfer probability formula:



In the formula, is the probability of ant K transferring from city I to city J; α and β were the relative importance of pheromones and heuristic factors, respectively. Is the pheromone quantity on edge (I, j); Is the heuristic factor; The next step for Ant K allows the selection of cities. The above formula is the pheromone update formula in the ant system, and is the pheromone quantity on the edge (I,j). ρ is the evaporation coefficient of pheromone, 0<ρ<1; Is the pheromone quantity left by the KTH ant on the edge (I,j) in this iteration; Q is a normal coefficient; Is the path length of the k ant during this tour.

In ant system, pheromone update formula is:



3. Solving steps of ant colony algorithm:

(1) Initialization parameters At the beginning of calculation, it is necessary to initialize related parameters, such as ant colony size (ant number) m, pheromone importance factor α, heuristic function importance factor β, pheromone will emit money ρ, total pheromone release Q, maximum iteration times iter_max, initial value of iteration times iter=1.

(2) construct solution space and randomly place each ant at different starting points. For each ant k (k=1,2,3… M), calculate the next city to be visited according to (2-1) until all ants have visited all cities.

(3) update information su to calculate the path length of each ant Lk(k=1,2… , m), record the optimal solution (shortest path) in the current iteration number. At the same time, pheromone concentration on the connection path of each city was updated according to Equations (2-2) and (2-3).

(4) Determine whether to terminate if iter<iter_max, set iter=iter+1 to clear the record table of ant paths and return to Step 2; Otherwise, the calculation is terminated and the optimal solution is output.

(5) Determine whether to terminate if iter<iter_max, set iter=iter+1 to clear the record table of ant paths and return to Step 2; Otherwise, the calculation is terminated and the optimal solution is output. 3. Determine whether to terminate. If iter<iter_max, set iter=iter+1 to clear the record table of ant paths and return to Step 2. Otherwise, the calculation is terminated and the optimal solution is output.

Ii. Source code

function varargout = untitled(varargin)
% UNTITLED M-file for untitled.fig
%      UNTITLED, by itself, creates a new UNTITLED or raises the existing
%      singleton*.
%
%      H = UNTITLED returns the handle to a new UNTITLED or the handle to
%      the existing singleton*.
%
%      UNTITLED('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in UNTITLED.M with the given input arguments.
%
%      UNTITLED('Property'.'Value',...). creates anew UNTITLED or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before untitled_OpeningFcn gets called.  An
%      unrecognized property name orinvalid value makes property application % stop. All inputs are passed to untitled_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 untitled

% Last Modified by GUIDE v2. 5 23-May- 2021. 10:03:35

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @untitled_OpeningFcn, ...
                   'gui_OutputFcn',  @untitled_OutputFcn, ...
                   'gui_LayoutFcn', [],...'gui_Callback'[]);if nargin && ischar(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
end

if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT


% --- Executes just before untitled is made visible.
function untitled_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 untitled (see VARARGIN)

% Choose default command line output for untitled
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);

% UIWAIT makes untitled wait for user response (see UIRESUME)
% uiwait(handles.figure1);


% --- Outputs from this function are returned to the command line.
function varargout = untitled_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 structure
varargout{1} = handles.output;



function edit1_Callback(hObject, eventdata, handles)
% hObject    handle to edit1 (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 edit1 as text
%        str2double(get(hObject,'String')) returns contents of edit1 as a double


% --- Executes during object creation, after setting all properties.
function edit1_CreateFcn(hObject, eventdata, handles)
% hObject    handle to edit1 (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 pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton1 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
x = [
0.054162803  3.278279079  1.269967423  5.161372814  6.554775334  7.802945518   89.91596827  169.1776572    143.304565
0.074421113  3.316697348  1.270329881  5.970864604  7.584967721  9.022343602   89.85758218  169.1213613    143.2747353
0.053348632  3.304197238  1.267854133  5.735004473  7.271149126  8.6360041     89.99066115  169.2467539    143.3392641
% 0.051720081  3.339632945  1.271724127  6.229903633  7.922718759  9.441330731   89.55978521  168.8424101    143.1321219
% 0.051864712  3.32076324   1.270382991  5.085988065  6.461152729  7.684102897   89.82998427  169.0964666    143.2626572
1.592002536  122.290451   1.734621006  28.65370822  49.70332419  64.63137481   151.3340327  219.4791589    158.962611
1.19861478   105.0456268  1.699966023  25.99113342  44.1840437   56.97791422   151.9810149  219.9476874    158.9929442
0.313453805  103.56017    1.74601649   25.72328554  44.91328074  58.52352399   152.2544307  220.1452556    159.0047857
% 1.74620714   104.5750915  1.722814423  26.09465184  44.95624257  58.40710523   155.5112779  222.4873381    159.1127213
% 0.800414929  115.6703271  1.75275249   25.50893937  44.71085699  58.1797557    154.2472348  221.5812885    159.0787792
1.139654266 327.4563137  3.296710797  32.35697041  106.6715737  168.3318025   170.8728098  233.2175981  158.7228117
3.177948091 401.7915242  3.349843446  34.95480454  117.0931229  184.2414168   177.5168284  237.7084602  158.0920229
0.10537445   358.1021167  3.403456107  33.08520829  112.6040542  179.1685128   177.1584012  237.4684609  158.1333957
% 1.345927915  346.0074742  3.277585069  34.71886501  113.7940336  178.5752461   177.8978973  237.9635592  158.0474411
% 0.98680132   352.4758738  3.226268588  35.43208236  114.3134143  178.6895352   174.8682892  235.9285934  158.3773425
11.91636275 763.3694263  2.163303163  66.74137606  144.3818299  197.3606479   178.1653349  238.1423511  158.0154829
10.83476732 642.6532934  2.224536056  59.143315    131.5664367  180.4907521   178.1435308  238.1277955  158.0181301
11.03075139 711.5078011  2.133406233  64.65685363  137.9393345  187.861062    175.3695323  236.2664234  158.326719
% 10.62874379 592.6236111  2.245205144  59.07819077  132.6426578  181.8505502   180.9528024  239.997917   157.6549507
% 15.28771972 792.6990829  2.333439781  55.9847639   130.6370752  180.1123338   182.4871818  241.0134313  157.4353917
 

% 0.051720081  3.339632945  1.271724127  6.229903633  7.922718759  9.441330731   89.55978521  168.8424101    143.1321219
];

a = get(handles.edit2,'String');
[c,flag]=str2num(a);

X=zeros(13.9);

X(1:12,:)=x(1:12, :); X(13,:)=c(1, :); R=get(handles.edit4,'String');
R=str2num(R);
t_max=get(handles.edit5,'String');
t_max=str2num(t_max);

 b=myant(X,R,t_max);
 unction edit2_Callback(hObject, eventdata, handles)
% hObject    handle to edit2 (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 edit2 as text
%        str2double(get(hObject,'String')) returns contents of edit2 as a double


% --- Executes during object creation, after setting all properties.
function edit2_CreateFcn(hObject, eventdata, handles)
% hObject    handle to edit2 (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 pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton2 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
 a = get(handles.edit3,'String');
 a=str2num(a);
 
 switch a
     case 1
         b=canshu('1.txt');
     case 2
         b=canshu('2.txt');
     case 3
         b=canshu('3.txt');
     case 4
         b=canshu('4.txt');
 end
    b=b';
    c=num2str(b);
%     ct=get(handles.edit2,'string');
%     empty(ct);
    set(handles.edit2,'String',c);
guidata(hObject, handles);
 



function edit3_Callback(hObject, eventdata, handles)
% hObject    handle to edit3 (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 edit3 as text
%        str2double(get(hObject,'String')) returns contents of edit3 as a double


% --- Executes during object creation, after setting all properties.
function edit3_CreateFcn(hObject, eventdata, handles)
% hObject    handle to edit3 (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
Copy the code

3. Operation results







Fourth, note

Version: 2014 a

Complete code or ghostwrite plus 1564658423