谁能解释一下这种混合 PSOGA 与普通 GA 有何不同？

Question

这段代码有没有变异、选择、交叉，就像原来的遗传算法一样

自此，混合算法（即 PSO 与 GA）是使用原始 GA 的所有步骤还是跳过一些

of them.Please 请告诉我。我对此很陌生，仍在努力理解。谢谢。

%%% 混合 GA 和 PSO 代码

function [gbest, gBestScore, all_scores] = QAP_PSO_GA(CreatePopFcn, FitnessFcn, UpdatePosition, ...
                                        nCity, nPlant, nPopSize, nIters)
    % Set algorithm parameters
    constant = 0.95;
    c1 = 1.5;       %1.4944;    %2;
    c2 = 1.5;       %1.4944;    %2;
    w = 0.792 * constant;
    % Allocate memory and initialize
    gBestScore = inf;
    all_scores = inf * ones(nPopSize, nIters);
    x = CreatePopFcn(nPopSize, nCity);
    v = zeros(nPopSize, nCity);
    pbest = x;
    % update lbest
    cost_p = inf * ones(1, nPopSize);  %feval(FUN, pbest');
    for i=1:nPopSize
        cost_p(i) = FitnessFcn(pbest(i, 1:nPlant));
    end
    lbest = update_lbest(cost_p, pbest, nPopSize);
    for iter = 1 : nIters    
        if mod(iter,1000) == 0
            parents = randperm(nPopSize);
            for i = 1:nPopSize
                x(i,:) = (pbest(i,:) + pbest(parents(i),:))/2;
%                v(i,:) = pbest(parents(i),:) - x(i,:);
%                v(i,:) = (v(i,:) + v(parents(i),:))/2;
            end

        else
            % Update velocity
            v = w*v + c1*rand(nPopSize,nCity).*(pbest-x) + c2*rand(nPopSize,nCity).*(lbest-x);
            % Update position
            x = x + v;
            x = UpdatePosition(x);
        end
        % Update pbest
        cost_x = inf * ones(1, nPopSize);
        for i=1:nPopSize
            cost_x(i) = FitnessFcn(x(i, 1:nPlant));
        end

        s = cost_x<cost_p;
        cost_p = (1-s).*cost_p + s.*cost_x;
        s = repmat(s',1,nCity);
        pbest = (1-s).*pbest + s.*x;
        % update lbest
        lbest = update_lbest(cost_p, pbest, nPopSize);
        % update global best
        all_scores(:, iter) = cost_x;
        [cost,index] = min(cost_p);
        if (cost < gBestScore) 
            gbest = pbest(index, :);
            gBestScore = cost;
        end

        % draw current fitness
        figure(1);
        plot(iter,min(cost_x),'cp','MarkerEdgeColor','k','MarkerFaceColor','g','MarkerSize',8)
        hold on

        str=strcat('Best fitness: ', num2str(min(cost_x)));
        disp(str);

    end
end
% Function to update lbest
function lbest = update_lbest(cost_p, x, nPopSize)
    sm(1, 1)= cost_p(1, nPopSize);
    sm(1, 2:3)= cost_p(1, 1:2);
    [cost, index] = min(sm);
    if index==1
        lbest(1, :) = x(nPopSize, :);
    else
        lbest(1, :) = x(index-1, :);
    end
    for i = 2:nPopSize-1
        sm(1, 1:3)= cost_p(1, i-1:i+1);
        [cost, index] = min(sm);
        lbest(i, :) = x(i+index-2, :);
    end
    sm(1, 1:2)= cost_p(1, nPopSize-1:nPopSize);
    sm(1, 3)= cost_p(1, 1);
    [cost, index] = min(sm);
    if index==3
        lbest(nPopSize, :) = x(1, :);
    else
        lbest(nPopSize, :) = x(nPopSize-2+index, :);
    end    
end

Answer 1

如果您是优化新手，我建议您先分别研究每个算法，然后您可能会研究如何将 GA 和 PSO 结合起来，尽管您必须具备基本的数学技能才能理解这两种算法的运算符并为了测试这些算法的效率（这才是真正重要的）。

此代码块负责父代选择和交叉：

            parents = randperm(nPopSize);
            for i = 1:nPopSize
                x(i,:) = (pbest(i,:) + pbest(parents(i),:))/2;
%                v(i,:) = pbest(parents(i),:) - x(i,:);
%                v(i,:) = (v(i,:) + v(parents(i),:))/2;
            end

选择 randperm 是如何完成的并不是很明显（我对 Matlab 没有经验）。

这是负责更新每个粒子的速度和位置的代码：

        % Update velocity
        v = w*v + c1*rand(nPopSize,nCity).*(pbest-x) + c2*rand(nPopSize,nCity).*(lbest-x);
        % Update position
        x = x + v;
        x = UpdatePosition(x);

这个版本的速度更新策略利用了所谓的 Interia-Weight W，这基本上意味着我们保留了每个粒子的速度历史（而不是完全重新计算它）。

值得一提的是，速度更新比交叉更频繁（每 1000 次迭代）。

谁能解释一下这种混合 PSOGA 与普通 GA 有何不同？

Can anyone explain how different is this hybrid PSOGA from normal GA?

algorithm

mutation

genetic

particle-swarm

crossover