【麻雀搜索算法】基于融合多策略改进的麻雀搜索算法求解单目标优化问题附matlab代码

原创

matlab科研助手 2022-05-09 21:42:52 博主文章分类：优化求解 ©著作权

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1 简介

针对麻雀搜索算法收敛速度缓慢,寻优精度不足和容易陷入局部最优等缺点,提出了一种融合正弦搜索策略和多样性变异处理策略的改进麻雀搜索算法.通过引入正弦搜索策略,自适应调整个体权重提高算法收敛速度;针对个体聚集程度过高问题,采用多样性变异处理,引入生物学中种群聚集度的概念和柯西变异对最优解进行扰动,提高算法逃离局部最优的可能.通过九个不同特征的基准函数进行寻优测试,测试结果表明改进算法能够更快地收敛于最优值,有更好的平均值和标准差,表明了其具备更优的收敛速度,收敛稳定性和逃离局部最优值的能力.

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),:);
end
curve=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;
end
Best_pos =GBestX;
Best_score = curve(end);
end