dpso粒子群matlab代码

DPSO（Dynamic Particle Swarm Optimization）是一种动态粒子群优化算法，它结合了粒子群优化算法和动态邻域搜索策略。下面是一个用MATLAB编写的DPSO算法的示例代码。

function [gbest, gbest_fit] = dpsopso(num_particles, num_dimensions, max_iter, lb, ub, c1, c2, w, r, alpha)
    % 初始化粒子群
    particles = lb + (ub - lb) * rand(num_particles, num_dimensions);
    velocities = zeros(num_particles, num_dimensions);
    pbests = particles;
    pbest_fits = inf(num_particles, 1);
    gbest = zeros(1, num_dimensions);
    gbest_fit = inf;

    for iter = 1:max_iter
        % 更新粒子的速度和位置
        for i = 1:num_particles
            velocities(i, :) = w * velocities(i, :) ...
                + c1 * rand() * (pbests(i, :) - particles(i, :)) ...
                + c2 * rand() * (gbest - particles(i, :));
            velocities(i, :) = min(max(velocities(i, :), lb - particles(i, :)), ub - particles(i, :)); % 速度限制在范围内
            particles(i, :) = particles(i, :) + velocities(i, :);
            particles(i, :) = max(min(particles(i, :), ub), lb); % 位置限制在范围内
        end

        % 计算粒子适应度，并更新个体最优和全局最优
        for i = 1:num_particles
            fit = fitness(particles(i, :)); % 计算适应度
            if fit < pbest_fits(i)
                pbests(i, :) = particles(i, :);
                pbest_fits(i) = fit;
            end
            if fit < gbest_fit
                gbest = particles(i, :);
                gbest_fit = fit;
            end
        end

        % 动态调整邻域搜索策略
        if mod(iter, r) == 0
            for i = 1:num_particles
                index = randperm(num_particles, alpha);
                neighborhood = pbests(index, :);
                neighborhood_fit = pbest_fits(index);
                [min_fit, min_index] = min(neighborhood_fit);
                if min_fit < pbest_fits(i)
                    pbests(i, :) = neighborhood(min_index, :);
                    pbest_fits(i) = min_fit;
                end
            end
        end

        disp(['Iteration ' num2str(iter) ': Best fitness = ' num2str(gbest_fit)]);
    end
end

% 适应度函数示例
function fit = fitness(x)
    % TODO: 根据具体问题定义适应度函数
end

这个DPSO代码实现了粒子群算法的基本框架，包括初始化、速度和位置的更新、个体最优和全局最优的更新、邻域搜索策略的动态调整等功能。你可以根据具体的问题，在适应度函数中定义自己的适应度评价方法。