Question

此代码是否像原始遗传算法一样具有变异，选择和交叉。

因此，混合算法（即具有GA的PSO）会使用原始GA的所有步骤，还是跳过某些步骤

其中

。请告诉我。我对此还很陌生，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

如果您是优化的新手，我建议您首先分别研究每种算法，然后研究GA和PSO如何结合使用，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，为了测试这些算法的效率（这才是真正的问题）。

此代码块负责父级选择和交叉：

            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的方式是不是很明显（我没有关于Matlab的经验）。

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);

此版本的速度更新策略利用的是Interia-Weight W，这基本上意味着我们正在保留每个粒子的速度历史记录（而不是完全重新计算）。

值得一提的是，速度更新比交叉更新（每1000次迭代）执行的频率更高。

谁能解释这种混合PSOGA与普通GA有何不同？

1 个答案: