%_________________________________________________________________________%% 麻雀
优化算法
%%_________________________________________________________________________%func
tion [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
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