I want the genetic algorithm to begin with intial parameter and this is not happened can you help me for making the genetic algorithm begin with the intial parameter?

% Main script for running the optimization loop
clear objectiveFunction;  % Clear persistent variables in the objective function
% Define desired deformation and force data 
desiredDeformation = [0.348347797, 1.173153205, 2.21822933, 3.812862584, ...
    4.803884609, 5.90554205, 6.953464755, 7.89172824, 8.664936558, ...
    9.714926366, 10.60098324, 11.59845866, 12.48718235, 13.32165075, ...
    14.26752652, 15.15919716, 15.88505542, 16.66788445, 17.34050795, ...
    18.01377124, 18.63121851, 19.13718778, 19.75680325, 20.43350892, ...
    20.88563395, 21.22577958, 21.79170524, 22.13235055, 22.41673682];
desiredForce = [-188.7501842, -200.5384007, -217.2529154, -232.9123719, ...
    -249.6333495, -267.3318313, -289.9889256, -309.6876558, -325.4440548, ...
    -344.1394294, -368.7967721, -392.4507591, -423.0506813, -454.6474963, ...
    -480.288806, -505.9365786, -530.6133097, -558.2548678, -588.8806413, ...
    -617.525555, -639.2439221, -658.994355, -687.6457315, -717.2810752, ... 
    -740.9996906, -765.7216616, -792.3986411, -815.1397523, -836.8968964];
% Plot desired deformation once
figure(1); % Create or select figure 1
clf;  % Clear current figure
hold on; % Hold on for multiple plots
plot(desiredDeformation, desiredForce, '-x', 'DisplayName', 'Desired Deformation', 'LineWidth', 1.5);
xlabel('Deformation');
ylabel('Force');
title('Comparison between Optimized and Desired Force-Deformation Curves'); 
legend('show');
grid on;
% Define number of variables and bounds
nVars = 11; % Number of variables: D1 to D11
lb = [9, 9, 12.5, 15, 17.5, 22.5, 25, 22.5, 17.5, 15, 12.5]; % Lower bounds for D1 to D11
ub = [30, 30, 40, 50, 60, 70, 100, 70, 60, 50, 30]; % Upper bounds for D1 to D11
% Define initial condition
initialCondition = [18, 18, 25, 30, 35, 45, 50, 45, 35, 30, 25];
% Replicate the initial condition across the population 
initialPopulation = repmat(initialCondition, 200, 1); % Create a 200x11 matrix where each row is the initial condition
% Set up the options for the GA, now using the full initial population matrix
options = optimoptions('ga', ...
    'PopulationSize', 200, ...% Set population size to 200 
    'CrossoverFraction', 0.7, ...
    'MutationFcn', @mutationadaptfeasible, ...
    'EliteCount', 1, ... % Only one individual to select
    'SelectionFcn', @selectiontournament, ...
    'MaxGenerations', 100, ...
    'Display', 'iter', ...
    'OutputFcn', @gaOutputFcn, ...
    'PlotFcn', {@gaplotbestf, @gaplotstopping}, ...
    'InitialPopulationMatrix', initialPopulation); % Use the replicated initial population
% Run the genetic algorithm 
[x_opt, fval] = ga(@(x)objectiveFunction(x, desiredForce), nVars, [], [], [], [], lb, ub, [], options);
% Display optimized parameters after obtaining them from the GA 
disp('Optimized Parameters:');
for i = 1:nVars 
    fprintf('D%d: %.2f\n', i, x_opt(i));
end
% Load the final optimized data from ANSYS 
optimizedDisplacement = readmatrix("E:\Barrel spring\Barrel spring.csv", 'Range', 'AP8:BR8');
optimizedForce = readmatrix("E:\Barrel spring\Barrel spring.csv", 'Range', 'M8:AO8');
% Plot the final optimized force-deformation curve
plot(optimizedDisplacement, optimizedForce, '-o', 'DisplayName', 'Final Optimized', 'LineWidth', 1.5);
% Update the legend and force MATLAB to update the figure immediately
legend('show');
drawnow; 
hold off;

[Walter Roberson]

I have formatted your code for you, which was a bit of a nuisance. In future, please press the > button in the CODE toolstrip, then paste in your formatted code into the code section that appears.

[noura]

Okay thank you

Answer 1

[John D'Errico]

I think you don't understand GA. It does not use a start point. It starts with an initial population of points from the sample space.

If you want a solver that uses a start point, then you might want to use fmincon, or one of the others.

If you really feel like you need to use GA, and you insist on providing a start point, then you will find an option in the initialPopulationMatrix parameter.

HOWEVER!!!!!

It appears you are using the initialPopulation parameter as just the same point, replicated 200 times. DO NOT DO THIS!!!!!! That will screw things up completely. Just ONE point is all that you need. GA will generate others, which will allow it to search the sample space properly.

[noura]

% Main script for running the optimization loop clear objectiveFunction; % Clear persistent variables in the objective function

% Define desired deformation and force data desiredDeformation = [0.348347797, 1.173153205, 2.21822933, 3.812862584, ... 4.803884609, 5.90554205, 6.953464755, 7.89172824, 8.664936558, ... 9.714926366, 10.60098324, 11.59845866, 12.48718235, 13.32165075, ... 14.26752652, 15.15919716, 15.88505542, 16.66788445, 17.34050795, ... 18.01377124, 18.63121851, 19.13718778, 19.75680325, 20.43350892, ... 20.88563395, 21.22577958, 21.79170524, 22.13235055, 22.41673682];

desiredForce = [-188.7501842, -200.5384007, -217.2529154, -232.9123719, ... -249.6333495, -267.3318313, -289.9889256, -309.6876558, -325.4440548, ... -344.1394294, -368.7967721, -392.4507591, -423.0506813, -454.6474963, ... -480.288806, -505.9365786, -530.6133097, -558.2548678, -588.8806413, ... -617.525555, -639.2439221, -658.994355, -687.6457315, -717.2810752, ... -740.9996906, -765.7216616, -792.3986411, -815.1397523, -836.8968964];

% Plot desired deformation once figure(1); % Create or select figure 1 clf; % Clear current figure hold on; % Hold on for multiple plots plot(desiredDeformation, desiredForce, '-x', 'DisplayName', 'Desired Deformation', 'LineWidth', 1.5); xlabel('Deformation'); ylabel('Force'); title('Comparison between Optimized and Desired Force-Deformation Curves'); legend('show'); grid on;

% Define number of variables and bounds nVars = 11; % Number of variables: D1 to D11 lb = [9, 9, 12.5, 15, 17.5, 22.5, 25, 22.5, 17.5, 15, 12.5]; % Lower bounds for D1 to D11 ub = [30, 30, 40, 50, 60, 70, 100, 70, 60, 50, 30]; % Upper bounds for D1 to D11

% Define initial condition initialCondition = [18, 18, 25, 30, 35, 45, 50, 45, 35, 30, 25]; % Replicate the initial condition across the population initialPopulation = randi(99, 200, numel(initialCondition)); % Create a 200x11 matrix where each row is the initial condition initialPopulation(1,:) = initialCondition;

% Set up the options for the GA, using only the initial condition options = optimoptions('ga', ... 'PopulationSize', 200, ... % Set population size to 200 'CrossoverFraction', 0.7, ... 'MutationFcn', @mutationadaptfeasible, ... 'EliteCount', 1, ... % Only one individual to select 'SelectionFcn', @selectiontournament, ... 'MaxGenerations', 100, ... 'Display', 'iter', ... 'OutputFcn', @gaOutputFcn, ... 'PlotFcn', {@gaplotbestf, @gaplotstopping}, ... 'InitialPopulationMatrix', initialCondition);

% Run the genetic algorithm [x_opt, fval] = ga(@(x)objectiveFunction(x, desiredForce), nVars, [], [], [], [], lb, ub, [], options);

% Display optimized parameters after obtaining them from the GA disp('Optimized Parameters:'); for i = 1:nVars fprintf('D%d: %.2f\n', i, x_opt(i)); end

% Load the final optimized data from ANSYS optimizedDisplacement = readmatrix("E:\Barrel spring\Barrel spring.csv", 'Range', 'AP8:BR8'); optimizedForce = readmatrix("E:\Barrel spring\Barrel spring.csv", 'Range', 'M8:AO8');

% Plot the final optimized force-deformation curve plot(optimizedDisplacement, optimizedForce, '-o', 'DisplayName', 'Final Optimized', 'LineWidth', 1.5);

% Update the legend and force MATLAB to update the figure immediately legend('show'); drawnow; hold off;

[noura]

I tried to edit it but the same problem still

[Walter Roberson]

please press the > button in the CODE toolstrip, then paste in your formatted code into the code section that appears. I am not interested in spending the time reformatting your code.

[noura]

>% Main script for running the optimization loop
clear objectiveFunction; % Clear persistent variables in the objective function

% Define desired deformation and force data
desiredDeformation = [0.348347797, 1.173153205, 2.21822933, 3.812862584, ...
    4.803884609, 5.90554205, 6.953464755, 7.89172824, 8.664936558, ...
    9.714926366, 10.60098324, 11.59845866, 12.48718235, 13.32165075, ...
    14.26752652, 15.15919716, 15.88505542, 16.66788445, 17.34050795, ...
    18.01377124, 18.63121851, 19.13718778, 19.75680325, 20.43350892, ...
    20.88563395, 21.22577958, 21.79170524, 22.13235055, 22.41673682];

desiredForce = [-188.7501842, -200.5384007, -217.2529154, -232.9123719, ...
    -249.6333495, -267.3318313, -289.9889256, -309.6876558, -325.4440548, ...
    -344.1394294, -368.7967721, -392.4507591, -423.0506813, -454.6474963, ...
    -480.288806, -505.9365786, -530.6133097, -558.2548678, -588.8806413, ...
    -617.525555, -639.2439221, -658.994355, -687.6457315, -717.2810752, ...
    -740.9996906, -765.7216616, -792.3986411, -815.1397523, -836.8968964];

% Plot desired deformation once
figure(1); % Create or select figure 1
clf; % Clear current figure
hold on; % Hold on for multiple plots
plot(desiredDeformation, desiredForce, '-x', 'DisplayName', 'Desired Deformation', 'LineWidth', 1.5);
xlabel('Deformation');
ylabel('Force');
title('Comparison between Optimized and Desired Force-Deformation Curves');
legend('show');
grid on;

% Define number of variables and bounds
nVars = 11; % Number of variables: D1 to D11
lb = [9, 9, 12.5, 15, 17.5, 22.5, 25, 22.5, 17.5, 15, 12.5]; % Lower bounds for D1 to D11
ub = [30, 30, 40, 50, 60, 70, 100, 70, 60, 50, 30]; % Upper bounds for D1 to D11

% Define initial condition
initialCondition = [18, 18, 25, 30, 35, 45, 50, 45, 35, 30, 25];
% Replicate the initial condition across the population 
initialPopulation = randi(99, 200, numel(initialCondition)); % Create a 200x11 matrix where each row is the initial condition
initialPopulation(1,:) = initialCondition;

% Set up the options for the GA, using only the initial condition
options = optimoptions('ga', ...
    'PopulationSize', 200, ...                   % Set population size to 200
    'CrossoverFraction', 0.7, ...
    'MutationFcn', @mutationadaptfeasible, ...
    'EliteCount', 1, ...                       % Only one individual to select
    'SelectionFcn', @selectiontournament, ...
    'MaxGenerations', 100, ...
    'Display', 'iter', ...
    'OutputFcn', @gaOutputFcn, ...
    'PlotFcn', {@gaplotbestf, @gaplotstopping}, ...
    'InitialPopulationMatrix', initialCondition); 

% Run the genetic algorithm
[x_opt, fval] = ga(@(x)objectiveFunction(x, desiredForce), nVars, [], [], [], [], lb, ub, [], options);

% Display optimized parameters after obtaining them from the GA
disp('Optimized Parameters:');
for i = 1:nVars
    fprintf('D%d: %.2f\n', i, x_opt(i));
end

% Load the final optimized data from ANSYS
optimizedDisplacement = readmatrix("E:\spring\spring.csv", 'Range', 'AP8:BR8');
optimizedForce = readmatrix("E:\spring\spring.csv", 'Range', 'M8:AO8');

% Plot the final optimized force-deformation curve
plot(optimizedDisplacement, optimizedForce, '-o', 'DisplayName', 'Final Optimized', 'LineWidth', 1.5);

% Update the legend and force MATLAB to update the figure immediately
legend('show');
drawnow;
hold off;