【图像语义分割】基于蜣螂优化算法DBO的Otsu(大津法)多阈值电表数字图像分割 电表数字识别【matlab代码】

% Dung Beetle Optimizer: (DBO) (demo) % Programmed by Jian-kai Xue % Updated 28 Nov.2022. function [bestX, fMin, Convergence_curve] = DBO(pop, M, c, d, dim, fobj) P_percent = 0.2; % 执行滚球行为的蜣螂比例 pNum = round(pop * P_percent); % 执行滚球行为的蜣螂数量 lb = c.*ones(1,dim); % 取值下限向量 ub = d.*ones(1,dim); % 取值上限向量 % 种群初始化 for i = 1 : pop x(i,:) = lb + (ub - lb) .* rand(1,dim); fit(i) = fobj(x(i,:)) ; end pFit = fit; pX = x; XX = pX; [fMin, bestI] = min(fit); % fMin是全局最优适应度值 bestX = x(bestI,:); % bestX是与fMin对应的全局最优解 % 开始迭代，进行个体更新 for t = 1 : M [fmax, B] = max(fit); worse = x(B,:); r2 = rand(1); % 滚球行为蜣螂的位置更新（分为无障碍物模式和有障碍物模式） for i = 1 : pNum if(r2<0.9) % ①无障碍物模式 a = rand(1,1); if (a>0.1) a = 1; else a = -1; end x(i,:) = pX(i,:)+0.3*abs(pX(i,:)-worse)+a*0.1*(XX(i,:)); else % ②有障碍物模式 aaa = randperm(180,1); if (aaa==0 ||aaa==90 ||aaa==180) x(i,:) = pX(i,:); end theta = aaa*pi/180; x(i,:) = pX(i,:)+tan(theta).*abs(pX(i,:)-XX(i,:)); end % 越界校正 x(i,:) = Bounds(x(i,:), lb, ub); fit(i) = fobj(x(i,:)); end [fMMin, bestII] = min(fit); % fMin是当前的最优适应度值 bestXX = x(bestII,:); % bestXX是当前的最优解 R = 1-t/M; Xnew1 = bestXX.*(1-R); Xnew2 = bestXX.*(1+R); Xnew1 = Bounds(Xnew1, lb, ub); Xnew2 = Bounds(Xnew2, lb, ub); % 繁殖行为蜣螂的位置更新 for i = (pNum + 1) : 12 x(i,:) = bestXX+((rand(1,dim)).*(pX(i,:)-Xnew1)+(rand(1,dim)).*(pX(i,:)-Xnew2)); x(i,:) = Bounds(x(i,:), Xnew1, Xnew2); fit(i) = fobj(x(i,:)) ; end Xnew11 = bestX.*(1-R); Xnew22 = bestX.*(1+R); Xnew11 = Bounds(Xnew11, lb, ub); Xnew22 = Bounds(Xnew22, lb, ub); % 觅食行为小蜣螂的位置更新 for i = 13 : 19 x(i,:) = pX(i,:)+((randn(1)).*(pX(i,:)-Xnew11)+((rand(1,dim)).*(pX(i,:)-Xnew22))); x(i,:) = Bounds(x(i,:), lb, ub); fit(i) = fobj(x(i,:)); end % 偷窃行为蜣螂的位置更新 for j = 20 : pop x(j,:) = bestX+randn(1,dim).*((abs((pX(j,:)-bestXX)))+(abs((pX(j,:)-bestX))))./2; x(j,:) = Bounds(x(j,:), lb, ub); fit(j) = fobj(x(j,:)) ; end % 更新个体最优值和全局最优值 XX = pX; for i = 1 : pop if (fit(i) < pFit(i)) pFit(i) = fit(i); pX(i,:) = x(i,:); end if( pFit(i) < fMin) fMin = pFit(i); bestX = pX(i,:); end end % 迭代曲线记录 Convergence_curve(t) = fMin; display(['第',num2str(t),'代的适应度值为: ', num2str(fMin)]); end % 越界校正函数 function s = Bounds(s, Lb, Ub) temp = s; I = temp < Lb; temp(I) = Lb(I); J = temp > Ub; temp(J) = Ub(J); s = temp;