% MFO-BP algorithm for predicting concrete strength
% Date: 2024-5-4
% User: Shuqian Wang
% --------------------------
function varargout = MFO_BP(varargin)
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @MFO_BP_OpeningFcn, ...
'gui_OutputFcn', @MFO_BP_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 MFO_BP is made visible.
function MFO_BP_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 MFO_BP (see VARARGIN)
% Choose default command line output for MFO_BP
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes MFO_BP wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = MFO_BP_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)
% --- Executes during object creation, after setting all properties.
function edit1_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit2_Callback(hObject, eventdata, handles)
% --- Executes during object creation, after setting all properties.
function edit2_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit3_Callback(hObject, eventdata, handles)
% --- Executes during object creation, after setting all properties.
function edit3_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit4_Callback(hObject, eventdata, handles)
% --- Executes during object creation, after setting all properties.
function edit4_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit5_Callback(hObject, eventdata, handles)
% --- Executes during object creation, after setting all properties.
function edit5_CreateFcn(hObject, eventdata, handles)
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)
%% Data preprocessing
predit_num = handles.predit;
train_num = handles.train_num;
test_num = handles.test_num;
filename = 'Data.xlsx'; % Excel filename
P = xlsread(filename); % Read data
save('Data.mat', 'P'); % Save as .mat file
P_train = P(1:train_num,1:6); % Rows 1-train_num are used as training data, and columns 1-6 of the input data dimension
T_train = P(1:train_num,predit_num); % Rows 1-train_num are used as training data, and columns predit_num as the final predicted indicator
P_test = P(1:test_num,1:6); % Rows 1-test_num are used as testing data, and columns 1-6 of the input data dimension
T_test = P(1:test_num,predit_num); % Rows 1-test_num are used as testing data, and columns predit_num as the final predicted indicator
data = P(:, 7:8);
handles.list.Data = P;
handles.list.ColumnName = {'MgO', 'HCSA', '水(t)', '水泥(t)', '沙子(t)', '水胶比', '7d抗压强度', '28d抗压强度'};
%% Get the number of nodes in the input and output layers
% Initialize the number of neurons in the hidden layer
inputnum = size(P_train,2); % Number of nodes in the input layer
hiddennum = 2 * inputnum + 1; % Number of nodes in the hidden layer
outputnum = size(T_train,2); % Number of nodes in the output layer
w1num = inputnum * hiddennum; % Number of weights from input to hidden layer
w2num = outputnum * hiddennum; % Number of weights from hidden to output layer
dim = w1num + hiddennum + w2num + outputnum; % Number of variables to be optimized
% warning('off')
%% Define algorithm parameters
N = 50; % Population size
Max_iteration = 50; % Maximum number of iterations
lb = -0.5; % Lower bound
ub = 0.5; % Upper bound
% Initialize moth positions
Moth_pos = initialization(N,dim,ub,lb);
Convergence_curve = zeros(1,Max_iteration);
Iteration = 1;
tic;
while Iteration < Max_iteration + 1
Flame_no = round(N - Iteration * ((N - 1)/Max_iteration)); % Calculate flame number based on iteration
for i = 1:size(Moth_pos,1)
% Check if moths are out of search space
Flag4ub = Moth_pos(i,:) > ub;
Flag4lb = Moth_pos(i,:) < lb;
Moth_pos(i,:) = (Moth_pos(i,:) .* (~(Flag4ub+Flag4lb))) + ub .* Flag4ub + lb .* Flag4lb;
% Calculate fitness function
X = Moth_pos(i,:);
Moth_fitness(1,i) = Objfun1(X, P_train, T_train, hiddennum, P_test, T_test);
% Moth_fitness(1,i)=fobj(Moth_pos(i,:));
end
if Iteration == 1
% Sort the first batch of moths
[fitness_sorted I] = sort(Moth_fitness);
sorted_population = Moth_pos(I,:);
% Update
best_flames = sorted_population;
best_flame_fitness = fitness_sorted;
else
% Sort
double_population = [previous_population;best_flames];
double_fitness = [previous_fitness best_flame_fitness];
[double_fitness_sorted I] = sort(double_fitness);
double_sorted_population = double_population(I,:);
fitness_sorted = double_fitness_sorted(1:N);
sorted_population = double_sorted_population(1:N,:);
% Update
best_flames = sorted_population;
best_flame_fitness = fitness_sorted;
end
% Update the best flame position obtained so far
Best_flame_score = fitness_sorted(1);
Best_flame_pos = sorted_population(1,:);
previous_population = Moth_pos;
previous_fitness = Moth_fitness;
% Update a linearly decreasing from -1 to -2
a = -1 + Iteration * ((-1)/Max_iteration);
for i = 1:size(Moth_pos,1)
for j = 1:size(Moth_pos,2)
if i <= Flame_no % Update moth position relative to respective flame
distance_to_flame = abs(sorted_population(i,j) - Moth_pos(i,j));
b = 1;
t = (a-1) * rand + 1;
Moth_pos(i,j) = distance_to_flame * exp(b.*t) .* cos(t.*2*pi) + sorted_population(i,j);
end
if i > Flame_no % Adjust moth position using a single flame
distance_to_flame = abs(sorted_population(i,j) - Moth_pos(i,j));
b = 1;
t = (a-1) * rand + 1;
Moth_pos(i,j) = distance_to_flame * exp(b.*t) .* cos(t.*2*pi) + sorted_population(Flame_no,j);
end
end
end
Convergence_curve(Iteration) = Best_flame_score;
% Display the iteration and best solution obtained so far
if m
