基于改进遗传算法求解非线性目标函数最小值问题含Matlab源码

% Directional Crossover (DX) for real parameter optimization % A. K. Das and D. K. Pratihar, "A directional crossover (DX) operator for real parameter % optimization using genetic algorithm," Applied Intelligence, vol. 49, pp. 1841-1865, 2019. function pop_crossover = DX( pop_selection, xmin, xmax,p_crossover_var,pc,p_crossover,mf,pd) [N, d] = size(pop_selection); % Population size & Number of variables pop_crossover = zeros( size(pop_selection) ); p = randperm(N); for ind=1:2:N p1 = p(ind); p2 = p(ind+1); parent1 = pop_selection(p1,:); parent2 = pop_selection(p2,:); child1 = zeros(1, d); child2 = zeros(1, d); if rand <= p_crossover % a pair is crossed with prob p_cross for i=1:d if rand<=p_crossover_var y3=pc(1,i); if (isequal( parent1(i),parent2(i) )==0) y1 = parent1(i); y2 = parent2(i); yl = xmin(i); yu = xmax(i); rand_var = (rand); val=1-((0.5)^((exp(abs(y2-y1)/(yu-yl))))); y4=0.5*(y1+y2); beta=rand_var/(mf^2); if(y3>=y4) if(rand<=pd) c1=val*(y1+y2)+((mf^(rand_var))*(exp(1-beta)))*(1-val)*abs(y2-y1); c2=(1-val)*(y1+y2)-((mf^(1-rand_var))*exp(-beta))*val*abs(y2-y1); else c1=val*(y1+y2)-((mf^(rand_var))*(exp(1-beta)))*(1-val)*abs(y2-y1); c2=(1-val)*(y1+y2)+((mf^(1-rand_var))*exp(-beta))*val*abs(y2-y1); end else if(rand<=pd) c1=val*(y1+y2)-((mf^(rand_var))*(exp(1-beta)))*(1-val)*abs(y2-y1); c2=(1-val)*(y1+y2)+((mf^(1-rand_var))*exp(-beta))*val*abs(y2-y1); else c1=val*(y1+y2)+((mf^(rand_var))*(exp(1-beta)))*(1-val)*abs(y2-y1); c2=(1-val)*(y1+y2)-((mf^(1-rand_var))*exp(-beta))*val*abs(y2-y1); end end if (c1 < yl), c1 = yl; end if (c2 < yl), c2 = yl; end if (c1 > yu), c1 = yu; end if (c2 > yu), c2 = yu; end if rand <= 0.5 child1(i) = c2; child2(i) = c1; else child1(i) = c1; child2(i) = c2; end else y4=parent1(i); if(y3~=y4) yl = xmin(i); yu = xmax(i); rand_var=rand; beta=rand_var/(mf^2); val=1-((0.5)^((exp(abs(y4-y3)/(yu-yl))))); if(rand<=pd) c1=val*(y3+y4)+((mf^(rand_var))*(exp(1-beta)))*(1-val)*(y3-y4); c2=(1-val)*(y3+y4)-((mf^(1-rand_var))*exp(-beta))*val*(y3-y4); else c1=val*(y3+y4)-((mf^(rand_var))*(exp(1-beta)))*(1-val)*(y3-y4); c2=(1-val)*(y3+y4)+((mf^(1-rand_var))*exp(-beta))*val*(y3-y4); end if (c1 < yl), c1 = yl; end if (c2 < yl), c2 = yl; end if (c1 > yu), c1 = yu; end if (c2 > yu), c2 = yu; end if rand <= 0.5 child1(i) = c2; child2(i) = c1; else child1(i) = c1; child2(i) = c2; end else child1(i) = parent1(i); child2(i) = parent2(i); end end else child1(i) = parent1(i); child2(i) = parent2(i); end end else child1 = parent1; child2 = parent2; end pop_crossover(ind,:) = child1; pop_crossover(ind+1,:) = child2; end