将以下代码转换为python：%精英替换 indexworst=find(fitness==max(fitness)); if length(indexworst)>1 indexworst=randsample(indexworst,1); end GApop1(indexworst,:)=elite(gen-1,:); fitness(indexworst,1)=elitefit(gen-1,:); fitness1=1./fitness; %精英保留 indexmin=find(fitness==min(fitness)); if length(indexmin)>1 indexmin=randsample(indexmin,1); end elite(gen,:)=GApop1(indexmin,:); %费用最小，适应度最大个体序号为indexmin elitefit(gen,:)=fitness(indexmin,:); bestfit(gen,1)=min(fitness); indexmin=find(fitness==min(fitness)); if length(indexmin)>1 indexmin=randsample(indexmin,1); end bestChrom(gen,:)=GApop1(indexmin,:); bestT(gen,:)=ztime(GApop1(indexmin,:),M,N,Tn0,Tn1,Q,ST0); bestT(gen,1) GApop0=GApop1; end bestindividual=bestChrom(maxgen,:) best=bestfit(maxgen,1) % plot(bestfit,'MarkerFaceColor','red','LineWidth',1); hold on set(gca,'color','none'); plot(bestfit(2:maxgen),'k','LineWidth',1); title('收敛曲线图(每一代最优费用）'); ylabel('最优费用'); xlabel('迭代次数'); toc;

indexworst = np.where(fitness == np.max(fitness))[0]
if len(indexworst) > 1:
    indexworst = np.random.choice(indexworst, 1)
GApop1[indexworst[0], :] = elite[gen-1, :]
fitness[indexworst[0], 0] = elitefit[gen-1, 0]
fitness1 = 1 / fitness
indexmin = np.where(fitness == np.min(fitness))[0]
if len(indexmin) > 1:
    indexmin = np.random.choice(indexmin, 1)
elite[gen, :] = GApop1[indexmin[0], :]
elitefit[gen, :] = fitness[indexmin[0], :]
bestfit[gen, 0] = np.min(fitness)
indexmin = np.where(fitness == np.min(fitness))[0]
if len(indexmin) > 1:
    indexmin = np.random.choice(indexmin, 1)
bestChrom[gen, :] = GApop1[indexmin[0], :]
bestT[gen, :] = ztime(GApop1[indexmin[0], :], M, N, Tn0, Tn1, Q, ST0)
bestT[gen, 0]
GApop0 = GApop1
bestindividual = bestChrom[maxgen, :]
best = bestfit[maxgen, 0]
plt.plot(bestfit, markerfacecolor='red', linewidth=1)
plt.hold(True)
plt.gca().set_color('none')
plt.plot(bestfit[1:maxgen], 'k', linewidth=1)
plt.title('收敛曲线图(每一代最优费用）')
plt.ylabel('最优费用')
plt.xlabel('迭代次数')
plt.show()