粒子群算法在matlab的代码
时间: 2023-06-01 13:01:42 浏览: 43
以下是粒子群算法在 MATLAB 中的代码示例:
% 初始化粒子群算法的参数
num_dimensions = 2; % 空间维度
num_particles = 20; % 粒子数量
max_iterations = 100; % 最大迭代次数
c1 = 2; % 加速常数1
c2 = 2; % 加速常数2
initial_velocity_range = [-1, 1]; % 初始速度范围
initial_position_range = [-10, 10]; % 初始位置范围
target_function = @(x) (x(1)^2 + x(2)^2); % 目标函数
% 初始化粒子群算法中的粒子
particles = struct();
particles.position = repmat(initial_position_range, num_particles, num_dimensions) + ...
rand(num_particles, num_dimensions) .* ...
repmat(diff(initial_position_range), num_particles, 1);
particles.velocity = repmat(initial_velocity_range, num_particles, num_dimensions) + ...
rand(num_particles, num_dimensions) .* ...
repmat(diff(initial_velocity_range), num_particles, 1);
particles.best_position = particles.position;
particles.best_fitness = zeros(num_particles, 1);
for i = 1:num_particles
particles.best_fitness(i) = target_function(particles.position(i, :));
end
[global_best_fitness, global_best_index] = min(particles.best_fitness);
global_best_position = particles.best_position(global_best_index, :);
% 开始迭代
for iteration = 1:max_iterations
% 更新粒子的速度和位置
for i = 1:num_particles
particles.velocity(i, :) = particles.velocity(i, :) + ...
c1 * rand(1, num_dimensions) .* ...
(particles.best_position(i, :) - particles.position(i, :)) + ...
c2 * rand(1, num_dimensions) .* ...
(global_best_position - particles.position(i, :));
particles.position(i, :) = particles.position(i, :) + particles.velocity(i, :);
% 确保粒子在可行区域内
particles.position(i, :) = max(particles.position(i, :), initial_position_range(1));
particles.position(i, :) = min(particles.position(i, :), initial_position_range(2));
% 更新粒子的最佳位置和最佳适应度
fitness = target_function(particles.position(i, :));
if fitness < particles.best_fitness(i)
particles.best_fitness(i) = fitness;
particles.best_position(i, :) = particles.position(i, :);
end
end
% 更新全局最佳位置和最佳适应度
[current_global_best_fitness, current_global_best_index] = min(particles.best_fitness);
if current_global_best_fitness < global_best_fitness
global_best_fitness = current_global_best_fitness;
global_best_index = current_global_best_index;
global_best_position = particles.best_position(global_best_index, :);
end
% 输出迭代信息
fprintf('Iteration %d: global best fitness = %f\n', iteration, global_best_fitness);
end
% 输出结果
fprintf('Optimal solution found at:\n');
disp(global_best_position);
fprintf('Optimal fitness value = %f\n', global_best_fitness);
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