MATLAB Code for the BAT Algorithm

% BAT Algorithm in MATLAB

% Parameters
num_bats = 30;
dim = 10;
num_iterations = 100;
freq_min = 0;
freq_max = 2;
A = 0.5;
r0 = 0.5;
alpha = 0.9;
gamma = 0.9;
lb = -10;
ub = 10;

% Initialize bat positions and velocities
positions = lb + (ub-lb).*rand(num_bats, dim);
velocities = zeros(num_bats, dim);
frequencies = zeros(num_bats, 1);
loudness = A * ones(num_bats, 1);
pulse_rate = r0 * ones(num_bats, 1);

% Evaluate initial fitness
fitness = arrayfun(@(i) sphere_func(positions(i, :)), 1:num_bats);
[best_fitness, idx] = min(fitness);
best_position = positions(idx, :);

for iteration = 1:num_iterations
    avg_loudness = mean(loudness);
    avg_pulse_rate = mean(pulse_rate);
    
    % Update bats
    for i = 1:num_bats
        beta = rand();
        frequencies(i) = freq_min + (freq_max - freq_min) * beta;
        velocities(i, :) = velocities(i, :) + (positions(i, :) - best_position) * frequencies(i);
        new_position = positions(i, :) + velocities(i, :);
        
        % Boundary check
        new_position = max(min(new_position, ub), lb);
        
        % Local search
        if rand() > pulse_rate(i)
            epsilon = rand() * 2 - 1; % Random value between -1 and 1
            new_position = positions(i, :) + epsilon * avg_loudness;
        end
        
        % Evaluate new solution
        new_fitness = sphere_func(new_position);
        
        % Greedy mechanism to update if new solution is better and random value is less than loudness
        if new_fitness < fitness(i) && rand() < loudness(i)
            positions(i, :) = new_position;
            fitness(i) = new_fitness;
        end
        
        % Update global best
        if fitness(i) < best_fitness
            best_position = positions(i, :);
            best_fitness = fitness(i);
        end
        
        loudness(i) = loudness(i) * alpha;
        pulse_rate(i) = r0 * (1 - exp(-gamma * iteration));
    end
    
    % Print the best fitness value in each iteration
    fprintf('Iteration %d: Best Fitness = %f\n', iteration, best_fitness);
end

fprintf('\nOptimized Solution: ');
disp(best_position);
fprintf('Best Fitness Value: %f\n', best_fitness);

function result = sphere_func(x)
    % x is a vector of input values
    % result is the value of the sphere function at x
    
    % Compute the sum of squares of elements in x
    result = sum(x.^2);
end