pso optimization in matlab in matlab

main.m
% Define the objective function
objective_function = @(x) x.^2;

% Define the number of variables
num_variables = 1;

% Define the boundaries of the variables
lb = -10; % Lower bound
ub = 10; % Upper bound

% Define the options for the optimization algorithm
options = optimoptions('particleswarm', 'SwarmSize', 100, 'MaxIterations', 100);

% Call the particleswarm function to perform the optimization
[x, fval] = particleswarm(objective_function, num_variables, lb, ub, options);

% Display the results
disp('Optimal solution:');
disp(x);
disp('Optimal function value:');
disp(fval);
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This is a simple example of how you can perform Particle Swarm Optimization (PSO) in Matlab. In this example, we define an objective function (in this case x^2), set the number of variables to 1, define the boundaries for the variable, set the options for the optimization algorithm, and then call the particleswarm function to find the optimal solution. Finally, we display the optimal solution and the optimal function value.

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