有没有MATLAB的混沌粒子群优化算法代码

是的，MATLAB有混沌粒子群优化算法的代码。以下是一个简单的实现示例：

function [x, fval] = cpsowl(fitnessfcn, dim, options)
% CPSO with logistic map
% fitnessfcn: objective function
% dim: number of decision variables
% options: CPSO options
% x: global optimal solution
% fval: global optimal value

% Set default options
defaultoptions = struct('PopulationSize', 20, 'MaxGenerations', 100, ...
    'InertiaWeight', 0.7298, 'SelfAdjustmentWeight', 1.496, ...
    'SocialAdjustmentWeight', 1.496, 'ConstrictionFactor', 0.7298, ...
    'Tolerance', 1e-6, 'Display', 'off');

% Merge user options with default options
if nargin < 3
    options = struct();
end
options = mergeoptions(defaultoptions, options);

% Initialize CPSO parameters
n = options.PopulationSize;
maxgen = options.MaxGenerations;
w = options.InertiaWeight;
c1 = options.SelfAdjustmentWeight;
c2 = options.SocialAdjustmentWeight;
phi = c1 + c2;
chi = 2 / abs(2 - phi - sqrt(phi^2 - 4*phi));
constriction = options.ConstrictionFactor * chi;
tol = options.Tolerance;
display = options.Display;

% Initialize population
x = rand(n, dim); % random initial positions
v = zeros(n, dim); % zero initial velocities
p = x; % personal best positions
fp = feval(fitnessfcn, p); % personal best values
[gfp, g] = min(fp); % global best position and value

% Main loop
for t = 1 : maxgen
    % Update velocity and position
    r1 = rand(n, dim);
    r2 = rand(n, dim);
    v = constriction * (w*v ...
        + c1*r1.*(p - x) ...
        + c2*r2.*(repmat(gfp, n, 1) - x));
    x = x + v;

    % Apply logistic map to position and velocity
    r = rand(n, dim);
    a = 4;
    b = 3.5699456;
    x = a * x .* (1 - x);
    v = b * v .* (1 - v) .* r;

    % Update personal best and global best
    fx = feval(fitnessfcn, x);
    improve = fx < fp;
    p(improve, :) = x(improve, :);
    fp(improve) = fx(improve);
    [improve, gf] = min(fp);
    if gf < gfp
        gfp = gf;
        g = improve;
    end

    % Check for convergence
    if gfp < tol
        break;
    end

    % Display progress
    if strcmp(display, 'iter')
        fprintf('Generation %d, Best Fitness: %g\n', t, gfp);
    end
end

% Return global best solution and value
x = p(g, :);
fval = gfp;
end

function options = mergeoptions(defaultoptions, options)
% Merge user options with default options

if isempty(options)
    options = struct();
end

optionnames = fieldnames(defaultoptions);
for i = 1 : length(optionnames)
    optionname = optionnames{i};
    if ~isfield(options, optionname)
        options.(optionname) = defaultoptions.(optionname);
    end
end
end

请注意，此代码是一种简单的实现，可能需要进行修改以满足您的特定需求。