pso优化算法的matlab完整代码

PSO (Particle Swarm Optimization) 是一种常用的优化算法，以下是 MATLAB 中实现的简单代码示例：

function [x, fval] = pso(fun, nvars, lb, ub)

% PSO parameters
n = 100; % number of particles
w = 0.729; % inertia weight
c1 = 1.49445; % cognitive parameter
c2 = 1.49445; % social parameter
maxiter = 100; % maximum number of iterations

% Initialize particles
x = repmat(lb, n, 1) + rand(n, nvars) .* repmat(ub - lb, n, 1);
v = zeros(n, nvars);
p = x;
pbest = inf(n, 1);

% Evaluate initial particles
for i = 1:n
    f = feval(fun, x(i,:));
    if f < pbest(i)
        p(i,:) = x(i,:);
        pbest(i) = f;
    end
end

% Main loop
for iter = 1:maxiter
    % Update particle velocities and positions
    for i = 1:n
        v(i,:) = w*v(i,:) + c1*rand(1,nvars).*(p(i,:) - x(i,:)) + c2*rand(1,nvars).*(pbest(i,:) - x(i,:));
        x(i,:) = x(i,:) + v(i,:);
        % Check boundaries
        x(i,:) = max(x(i,:), lb);
        x(i,:) = min(x(i,:), ub);
    end
    % Evaluate particles
    for i = 1:n
        f = feval(fun, x(i,:));
        if f < pbest(i)
            p(i,:) = x(i,:);
            pbest(i) = f;
        end
    end
    % Update global best
    [fval, gbest] = min(pbest);
    xbest = p(gbest,:);
    % Check convergence
    if iter > 1 && abs(fval - fval_old) < 1e-6
        break
    end
    fval_old = fval;
end
x = xbest;

其中，输入参数为：

- `fun`：优化函数句柄
- `nvars`：变量个数
- `lb`：变量下界
- `ub`：变量上界

输出参数为：

- `x`：最优解
- `fval`：最优解对应的函数值

例如，对于 Rosenbrock 函数，可以使用以下代码进行优化：

fun = @(x) 100*(x(2)-x(1)^2)^2 + (1-x(1))^2;
nvars = 2;
lb = [-5 -5];
ub = [5 5];
[x, fval] = pso(fun, nvars, lb, ub);

参考文献：

- Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks (pp. 1942-1948). DOI: 10.1109/ICNN.1995.488968