JADE算法matlab代码

以下是JADE算法的Matlab代码：

function [best_x, best_fval, output] = JADE(fitness_func, x0, lb, ub, max_evals, pop_size, c, p)
% JADE: JADE Algorithm for optimization problems
% fitness_func: function handle for fitness evaluation
% x0: initial population, each row is a candidate solution
% lb: lower bound of the search space
% ub: upper bound of the search space
% max_evals: maximum number of fitness evaluations
% pop_size: population size
% c: scaling factor
% p: crossover probability
% best_x: best solution found
% best_fval: best objective function value found
% output: structure containing convergence information

% Calculate dimensionality and initialize population
[n_dim, n_pop] = size(x0);
pop = x0;

% Initialize convergence information
output.best_fval = Inf(max_evals, 1);
output.mean_fval = zeros(max_evals, 1);

% Evaluate initial population
fvals = zeros(n_pop, 1);
for i = 1:n_pop
    fvals(i) = fitness_func(pop(:, i));
end

% Set initial best solution and objective function value
[best_fval, best_idx] = min(fvals);
best_x = pop(:, best_idx);

% Save convergence information
output.best_fval(1) = best_fval;
output.mean_fval(1) = mean(fvals);

% Initialize iteration counter
evals = n_pop;

% Main loop
while evals < max_evals
    % Generate trial solutions
    u = zeros(n_dim, n_pop);
    for i = 1:n_pop
        % Select three distinct random vectors
        r = randperm(n_pop, 3);
        while any(r == i)
            r = randperm(n_pop, 3);
        end
        % Generate trial solution
        v = pop(:, r(1)) + c * (pop(:, r(2)) - pop(:, r(3)));
        % Perform crossover
        j_rand = randi(n_dim);
        t = rand(n_dim, 1) < p;
        t(j_rand) = true;
        u(:, i) = t .* v + (1 - t) .* pop(:, i);
    end
    
    % Evaluate trial solutions
    trial_fvals = zeros(n_pop, 1);
    for i = 1:n_pop
        trial_fvals(i) = fitness_func(u(:, i));
        evals = evals + 1;
        if evals >= max_evals
            break;
        end
    end
    
    % Update population
    for i = 1:n_pop
        if trial_fvals(i) < fvals(i)
            pop(:, i) = u(:, i);
            fvals(i) = trial_fvals(i);
            if fvals(i) < best_fval
                best_fval = fvals(i);
                best_x = pop(:, i);
            end
        end
    end
    
    % Save convergence information
    output.best_fval(evals) = best_fval;
    output.mean_fval(evals) = mean(fvals);
end

end

该代码实现了 JADE 算法的基本思想，包括初始化种群、生成和评估试验解、更新种群等步骤，并且支持搜索空间边界限制。在使用时需要提供适应度函数、初始种群、搜索空间边界、最大评估次数、种群大小、缩放因子和交叉概率等参数。输出包括最优解、最优目标函数值和收敛信息。