pso algorithm in matlab

main.m
% Define objective function
f = @(x) sum(x.^2);

% Define PSO parameters
num_dims = 2;
num_particles = 20;
max_iter = 100;
w = 0.7;
c1 = 1.5;
c2 = 1.5;
lb = -10 * ones(1, num_dims);
ub = 10 * ones(1, num_dims);

% Initialize particles
pos = lb + rand(num_particles, num_dims) .* (ub - lb);
vel = zeros(num_particles, num_dims);
best_pos = pos;
best_val = inf(num_particles, 1);

% Run PSO algorithm
for iter = 1:max_iter
    % Evaluate objective function
    vals = f(pos);
    
    % Update personal best
    update = vals < best_val;
    best_val(update) = vals(update);
    best_pos(update, :) = pos(update, :);
    
    % Update global best
    [global_best_val, global_best_idx] = min(best_val);
    global_best_pos = best_pos(global_best_idx, :);
    
    % Update velocity and position
    r1 = rand(num_particles, num_dims);
    r2 = rand(num_particles, num_dims);
    vel = w * vel + c1 * r1 .* (best_pos - pos) + c2 * r2 .* (global_best_pos - pos);
    pos = pos + vel;
    
    % Enforce bounds
    pos = max(pos, lb);
    pos = min(pos, ub);
end

% Print results
disp(['Best solution: ', num2str(global_best_pos)]);
disp(['Best objective value: ', num2str(global_best_val)]);
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