1 简介

## 2 部分代码

%_________________________________________________________________________%% 原始麻雀优化算法SSA %%_________________________________________________________________________%function [Best_pos,Best_score,curve]=SSA(pop,Max_iter,lb,ub,dim,fobj)ST = 0.6; % 预警值PD = 0.7; % 发现者的比列，剩下的是加入者SD = 0.2; % 意识到有危险麻雀的比重PDNumber = round(pop*PD); % 发现者数量SDNumber = round(pop*SD); % 意识到有危险麻雀数量if(max(size(ub)) == 1)   ub = ub.*ones(1,dim);   lb = lb.*ones(1,dim);  end% 种群初始化X0=initialization(pop,dim,ub,lb);X = X0;% 计算初始适应度值fitness = zeros(1,pop);for i = 1:pop   fitness(i) =  fobj(X(i,:));end[fitness, index]= sort(fitness); % 排序BestF = fitness(1);WorstF = fitness(end);GBestF = fitness(1); % 全局最优适应度值for i = 1:pop    X(i,:) = X0(index(i),:);endcurve=zeros(1,Max_iter);GBestX = X(1,:); % 全局最优位置X_new = X;for i = 1: Max_iter    BestF = fitness(1);    WorstF = fitness(end);    R2 = rand(1);   for j = 1:PDNumber      if(R2<ST)          X_new(j,:) = X(j,:).*exp(-j/(rand(1)*Max_iter));      else          X_new(j,:) = X(j,:) + randn()*ones(1,dim);      end        end   for j = PDNumber+1:pop%        if(j>(pop/2))        if(j>(pop - PDNumber)/2 + PDNumber)          X_new(j,:)= randn().*exp((X(end,:) - X(j,:))/j^2);       else          % 产生-1，1的随机数          A = ones(1,dim);          for a = 1:dim            if(rand()>0.5)                A(a) = -1;            end          end           AA = A'*inv(A*A');               X_new(j,:)= X(1,:) + abs(X(j,:) - X(1,:)).*AA';       end   end   Temp = randperm(pop);   SDchooseIndex = Temp(1:SDNumber);    for j = 1:SDNumber       if(fitness(SDchooseIndex(j))>BestF)           X_new(SDchooseIndex(j),:) = X(1,:) + randn().*abs(X(SDchooseIndex(j),:) - X(1,:));       elseif(fitness(SDchooseIndex(j))== BestF)           K = 2*rand() -1;           X_new(SDchooseIndex(j),:) = X(SDchooseIndex(j),:) + K.*(abs( X(SDchooseIndex(j),:) - X(end,:))./(fitness(SDchooseIndex(j)) - fitness(end) + 10^-8));       end   end   % 边界控制   for j = 1:pop       for a = 1: dim           if(X_new(j,a)>ub(a))               X_new(j,a) =ub(a);           end           if(X_new(j,a)<lb(a))               X_new(j,a) =lb(a);           end       end   end    % 更新位置   for j=1:pop    fitness_new(j) = fobj(X_new(j,:));   end   for j = 1:pop    if(fitness_new(j) < GBestF)       GBestF = fitness_new(j);        GBestX = X_new(j,:);       end   end   X = X_new;   fitness = fitness_new;   % 排序更新   [fitness, index]= sort(fitness); % 排序   BestF = fitness(1);   WorstF = fitness(end);   for j = 1:pop      X(j,:) = X(index(j),:);   end   curve(i) = GBestF;endBest_pos =GBestX;Best_score = curve(end);end

## 4 参考文献

[1]付华, 刘昊. 多策略融合的改进麻雀搜索算法及其应用[J]. 控制与决策, 2022, 37(1):10.