for i=1:popsize pc=min(max(parent_selected(i,N+M+2)/rank_max,0.1),0.5); if parent_selected(i,N+M+2)==1% ir=ceil(lrank1*rand()); % while parent_selected(i,N+1:N+M)==BestParent_pool(ir,N+1:N+M) % ir=ceil(lrank1*rand()); % end Parentr=BestParent_pool(ir,:); for j=1:N if rand()<pc S_crossover(i,j)=parent_selected(i,j)+rand()*(BestParent_pool(ir,j)-parent_selected(i,j)); else S_crossover(i,j)=parent_selected(i,j); end end else for j=1:N if rand()<pc S_crossover(i,j)=parent_selected(i,j)+2*rand()*(BS(j)-parent_selected(i,j));%BS(j);% % stpj=abs((BS(j)-parent_selected(i,j)))/2; % S_crossover(i,j)=BS(j)-stpj+2*stpj*rand(); jm=mod(j,J); if jm==0 jm=J; end if S_crossover(i,j)>xmax(jm) S_crossover(i,j)=xmax(jm); elseif S_crossover(i,j)<xmin(jm) S_crossover(i,j)=xmin(jm); end else S_crossover(i,j)=parent_selected(i,j); end end end end

标准微粒群算法实现：Goldstein-Price函数优化

"091附录1、标准微粒群算法源程序.pdf" 这篇文章将深入探讨标准微粒群算法（PSO，Particle Swarm Optimization），它是一种基于群体智能的优化算法，灵感来源于鸟群或鱼群的行为。在给定的源代码中，该程序旨在寻找...

遗传算法求解多重旅行商问题 - MTSP_GA教程与实现

1. 多重旅行推销员问题 (M-TSP) 概述多重旅行推销员问题（M-TSP）是经典的旅行推销员问题（TSP）的扩展，其中存在多个旅行推销员，并且每个推销员需要访问一组独特的城市并最终返回到起始城市。在M-TSP中，目标是在...

rank_max=max(parent_selected(:,N+M+2)); for i=1:popsize pc=min(max(parent_selected(i,N+M+2)/rank_max,0.1),0.5); if parent_selected(i,N+M+2)==1% ir=ceil(lrank1rand()); % while parent_selected(i,N+1:N+M)==BestParent_pool(ir,N+1:N+M) % ir=ceil(lrank1rand()); % end Parentr=BestParent_pool(ir,:); for j=1:N if rand()xmax(jm) S_crossover(i,j)=xmax(jm); elseif S_crossover(i,j)<xmin(jm) S_crossover(i,j)=xmin(jm); end else S_crossover(i,j)=parent_selected(i,j); end end end end

- 如果个体的非支配排序为1（即 parent_selected(i,N+M+2)==1），则执行以下步骤： - 计算 pc，即将个体的非支配排序除以 rank_max，并限制其在0.1和0.5之间。 - 从 BestParent_pool 中随机选择一个解作为...

% selection (Parent Pt of 'N' pop size) ind1=randperm(popsize)'; ind2=randperm(popsize)'; popp1=population(ind1,:); popp2=population(ind2,:); popp=[popp1;popp2]; for i=1:popsize if popp(2i-1,N+M+2)~=popp(2i,N+M+2) if popp(2i-1,N+M+2)popp(2i,N+M+3) parent_selected(i,:)=popp(2i-1,:); else parent_selected(i,:)=popp(2i,:); end else if rand()<0.5 parent_selected(i,:)=popp(2i-1,:); else parent_selected(i,:)=popp(2i,:); end end end end ind_rank1=find(parent_selected(:,N+M+2)==1);lrank1=length(ind_rank1);%非支配排序为1的解及个数 BestParent_pool=parent_selected(ind_rank1,:); if lrank1~=1 fmaxM=max(BestParent_pool(:,N+1:N+M)); fminM=min(BestParent_pool(:,N+1:N+M)); if fmaxM~=fminM fnsum=sqrt(((BestParent_pool(:,N+1)-fminM(1))/(fmaxM(1)-fminM(1))).^2+((BestParent_pool(:,N+2)-fminM(2))/(fmaxM(2)-fminM(2))).^2); indb1=find(fnsum==min(fnsum));indb=indb1(1); BS=parent_selected(indb,:); else BS=BestParent_pool(1,:); end else BS=parent_selected(ind_rank1,:); end

对于每个个体i，首先比较popp(2*i-1,N+M+2)和popp(2*i,N+M+2)，如果popp(2*i-1,N+M+2)小于popp(2*i,N+M+2)，则选择popp(2*i-1,:)作为父代个体，否则选择popp(2*i,:)作为父代个体。如果popp(2*i-1,N+M+2)和popp(2*i...

rowsindex=0; for i=1:rank-1 l_f=length(front(i).fr); if rowsindex+l_f>popsize Sol=chromosome_sorted(1:rowsindex+l_f,:); %normalization fobjmin=min(Sol(:,N+1:N+M)); fobjmax=max(Sol(:,N+1:N+M)); for lr=1:rowsindex+l_f Sol(lr,N+M+2:N+M+3)=(Sol(lr,N+1:N+2)-fobjmin)./(fobjmax-fobjmin); end ind_mind=zeros(1,rowsindex+l_f); disM=Inf*ones(rowsindex+l_f,rowsindex+l_f); for lp=1:rowsindex+l_f-1 for lq=lp+1:rowsindex+l_f disM(lp,lq)=norm(Sol(lp,N+M+2:N+M+3)-Sol(lq,N+M+2:N+M+3)); disM(lq,lp)=disM(lp,lq); end end for lr=1:rowsindex+l_f indlr1=find(disM(lr,:)==min(disM(lr,:)));indlr=indlr1(1); ind_mind(lr)=indlr;%与个体lr距离最小的个体为indlr chromosome_sorted(lr,N+M+4)=min(disM(lr,:)); end indb1=find(Sol(:,N+1)==min(Sol(:,N+1))); indb2=find(Sol(:,N+2)==min(Sol(:,N+2))); chromosome_sorted(indb1,N+M+4)=Inf; chromosome_sorted(indb2,N+M+4)=Inf; break; end rowsindex=rowsindex+l_f; end % chromosome_sorted(:,N+M+4)=sum(chromosome_sorted(:,N+M+2:N+M+3),2); % chromosome_sorted(:,N+M+4)=sqrt(chromosome_sorted(:,N+M+2).^2+chromosome_sorted(:,N+M+3).^2); chromosome_NDS_CD1=[chromosome_sorted(:,1:N+M) zeros(popsize1,1) chromosome_sorted(:,N+M+1) chromosome_sorted(:,N+M+4)]; % Final Output Variable end

- 提取已处理的染色体Sol，包括前N列（表示基因）和第N+1到N+M列（表示目标函数值）。 - 对Sol中的目标函数值进行归一化，即将目标函数值映射到[0, 1]的范围内。首先找出Sol中目标函数值的最小值和最大值，并分别...

将以下代码转换为python：for gen=2:maxgen %选择操作 GApop1=zselect(GApop0,popsize,fitness1); %交叉操作 GApop1=zcross(GApop1,popsize,pc1,pc2,fitness1,M,N,Tn0,Tn1,Q,ST0,maxT,gen,maxgen); %变异操作 GApop1=zmutate(GApop1,popsize,pm1,pm2,fitness1,M,N,Tn0,Tn1,Q,ST0,maxT,gen,maxgen,LCR,ECR,MCR,FC,ICR); for j=1:popsize a(j,:)=ztime(GApop1(j,:),M,N,Tn0,Tn1,Q,ST0); fitness(j,1)=zcost(GApop1(j,:),M,N,a(j,1),a(j,2:2M+3),a(j,2M+4:2M+2+N),LCR,ECR,MCR,FC,ICR,Q); end for j=1:popsize if a(j,1)>maxT fitness(j)=10^100;%fitness(j)exp(10^(a(j,1)-maxT)); end end %%%%%%%%灾变算子 if elitefit(gen,:)==elitefit(gen-1,:) Ca=Ca-1; else Ca=10; %重新灾变倒计时 end if Ca==0 %发生灾变 for c=1:0.1popsize GApop1(c,:)=GApop1(randpopsize,:); end for c=1:0.9popsize GApop1(c+1,:)=zcode(M,N); end %重新计算适应度值 for j=1:popsize a(j,:)=ztime(GApop1(j,:),M,N,Tn0,Tn1,Q,ST0); fitness(j,1)=zcost(GApop1(j,:),M,N,a(j,1),a(j,2:2M+3),a(j,2M+4:2M+2+N),LCR,ECR,MCR,FC,ICR,Q); end for j=1:popsize if a(j,1)>maxT fitness(j)=10^100;%fitness(j)*exp(10^(a(j,1)-maxT)); end end end %%%%%%%%

GApop1 = zcross(GApop1, popsize, pc1, pc2, fitness1, M, N, Tn0, Tn1, Q, ST0, maxT, gen, maxgen) # 交叉操作 GApop1 = zmutate(GApop1, popsize, pm1, pm2, fitness1, M, N, Tn0, Tn1, Q, ST0, maxT, gen, ...

将以下代码转换为python：for i=1:GSAmaxgen tonefor0=cputime; i for j=1:popsize fitness(j,1)=funt(Q,pr0,GSApop0(j,:),M,NM,BL,N,L,pre,f,AT,l); fitness(j,2)=func(GSApop0(j,:),M,NM,N,LCR); end averfit=sum(fitness(:,1))/popsize; elitefit=min(fitness(:,1)); FIT=[elitefit,averfit];

GSApop0 = np.zeros((popsize, (M+NM)*N+M)) M = ... NM = ... BL = ... N = ... L = ... pre = ... f = ... AT = ... l = ... LCR = ... for i in range(1, GSAmaxgen+1): tonefor0 = time.process_time() print...

将以下代码转化为python：function newpop=zselect(pop,popsize,fitness) sumfit=sum(fitness); pfit=fitness./sumfit; %每个个体的选择概率 pfit=cumsum(pfit);%累积概率 index=[]; for i=1:popsize %转popsize次轮盘 pick=rand; while pick==0 pick=rand; end for j=1:popsize %寻找本次生成的随机数<累积选择概率的染色体 pick=pick-pfit(j); if pick<0 index=[index j];%index和j的交集 break; end end end newpop=pop(index,:); end

for i in range(popsize): pick = np.random.rand() # 生成随机数 while pick == 0: pick = np.random.rand() for j in range(popsize): if pick [j]: # 寻找随机数小于累积选择概率的染色体 index.append(j)...

将以下代码转换为python：%产生初始种群 flag11=1; while flag11==1 GApop0=zeros(popsize,8M+6); for i=1:popsize GApop0(i,:)=zcode(M,N); end fitness=zeros(popsize,1);%费用 fitness1=zeros(popsize,1);%适应度函数 a=zeros(popsize,2M+3+N-1); %工期 bestChrom=zeros(maxgen,8M+6); bestfit=zeros(maxgen,1); %每代最优费用 bestT=zeros(maxgen,2M+3+N-1); %每代最优费用对应的工期 elite=zeros(maxgen,8M+6); %精英解 elitefit=zeros(maxgen,1); %精英值 for j=1:popsize a(j,:)=ztime(GApop0(j,:),M,N,Tn0,Tn1,Q,ST0); fitness(j,1)=zcost(GApop0(j,:),M,N,a(j,1),a(j,2:2M+3),a(j,2M+4:2M+2+N),LCR,ECR,MCR,FC,ICR,Q); end for j=1:popsize if a(j,1) > maxT fitness(j,1)=10^100; end end if all(fitness(:,1)==10^100) flag11=1; %重新生成初始种群 else flag11=0; end end fitness1=1./fitness; %适应度值取倒数，成本最小，倒数最大 %精英选择 indexmin=find(fitness==min(fitness));%查找非零元素的索引和值 if length(indexmin)>1 indexmin=randsample(indexmin,1);%从整数1到indexmin中无放回随机均匀抽取1个值。 end elite(1,:)=GApop0(indexmin,:); elitefit(1,:)=fitness(indexmin,:);

fitness[j, 0] = zcost(GApop0[j, :], M, N, a[j, 0], a[j, 1:2*M+3], a[j, 2*M+4:2*M+2+N], LCR, ECR, MCR, FC, ICR, Q) for j in range(popsize): if a[j, 0] > maxT: fitness[j, 0] = 10 ** 100 if np.all...

将以下代码转换为python并解释其含义：%产生初始种群 flag11=1; while flag11==1 GApop0=zeros(popsize,8M+6); for i=1:popsize GApop0(i,:)=zcode(M,N); end fitness=zeros(popsize,1);%费用 fitness1=zeros(popsize,1);%适应度函数 a=zeros(popsize,2M+3+N-1); %工期 bestChrom=zeros(maxgen,8M+6); bestfit=zeros(maxgen,1); %每代最优费用 bestT=zeros(maxgen,2M+3+N-1); %每代最优费用对应的工期 elite=zeros(maxgen,8*M+6); %精英解 elitefit=zeros(maxgen,1); %精英值

a = np.zeros((popsize, 2*M+3+N-1)) # 工期 bestChrom = np.zeros((maxgen, 8*M+6)) # 每代最优费用 bestfit = np.zeros((maxgen, 1)) # 每代最优费用对应的工期 bestT = np.zeros((maxgen, 2*M+3+N-1)) # 精英...

initial_similarity = 0; for i = 1:PopSize population(i,:)=initial(randperm(length(initial(:)))); end mpopulation = unique(population,'row'); initial_similarity = size(mpopulation,1)/size(population,1);

具体来说，代码中的变量 initial 是种群中每个个体的初始化基因序列，PopSize 是种群大小。首先，对于每个个体，将其基因序列随机打乱，然后加入到种群中。接着，通过 unique 函数去除重复的个体，得到一个无...

将下面MATLAB代码转换成python代码 %% 基于logistic映射的种群初始化子函数 function Positions=logisticInitialization(popsize,dim,ub,lb) %input：popsize 种群数量 % dim 变量维度 % ub 变量上限 % lb 变量下限 %return：Positions 生成的初始种群位置 %初始化位置0数组 Positions=zeros(popsize,dim); %对每个个体，混沌映射产生位置 for i = 1:popsize value = Logistic(dim); %混沌映射序列 Positions(i,:)=value.(ub-lb)+lb; %位置越界限制 Positions(i,:)=min(Positions(i,:),ub); %上界调整 Positions(i,:)=max(Positions(i,:),lb); %下界调整 end end %混沌映射子函数 function sequence=Logistic(n) %input：n 混沌序列长度 %return：value 生成的混沌序列 %初始化数组 sequence=zeros(1,n); sequence(1)=rand; %序列起点 %x0不为(0,0.25,0.5,0.75,1) while max(sequence(1)==[0 0.25 0.5 0.75 1])==1 sequence(1)=rand; end mu =3.8 ;%参数mu范围(0,4) for i=1:n-1 sequence(i+1)=musequence(i)*(1-sequence(i)); end end

Here's the Python code equivalent to the given MATLAB code: python import numpy as np ... for i in range(n-1): sequence[i+1] = mu * sequence[i] * (1 - sequence[i]) return sequence

将如下代码转换为python：avefit=sum(fitness1)/popsize; for i=1:(popsize/2) %选择二分之一popsize次，每次两条，输出两条，没交叉的也算输出 %随机选择两个染色体进行交叉 pick=rand(1,2); while prod(pick)==0 pick=rand(1,2); %保证生成的两个随机数同时非零 end index=ceil(pick.*popsize); f1=max(fitness1(index)); if f1<=avefit

for i in range(popsize // 2): # 选择二分之一popsize次，每次两条，输出两条，没交叉的也算输出 # 随机选择两个染色体进行交叉 pick = np.random.rand(2) while np.prod(pick) == 0: # 保证生成的两个随机数...

将以下代码转换为python：for j=1:popsize fitness(j,1)=funt(Q,pr0,GSApop0(j,:),M,NM,BL,N,L,pre,f,AT,l); fitness(j,2)=func(GSApop0(j,:),M,NM,N,LCR);

在这段代码中，假设 popsize 是一个整数变量，而 Q、pr0、GSApop0、M、NM、BL、N、L、pre、f、AT、l 和 LCR 是已定义的变量或数组。通过循环遍历 j 的值从 1 到 popsize，分别计算...

题目为： min⁡ [𝑧=𝑥_1^2+𝑥_2^2+3𝑥_3^2+4𝑥_4^2+2𝑥_5^2−8𝑥_1−2𝑥_2−2𝑥_3−𝑥_4−2𝑥_5 ] 𝑠.𝑡.{ 0 <= x_i <= 99，且x_i 为整数，i =1，2，3，...，5， 𝑥_1+𝑥_2 +𝑥_3 +𝑥_4 +𝑥_5≤400， 𝑥_1+2𝑥_2 +2𝑥_3 +𝑥_4 +6𝑥_5≤800， 2𝑥_1+𝑥_2 +6𝑥_3 <= 200， 𝑥_3+𝑥_4 +5𝑥_5≤200 } 用matlab语言实现遗传算法求解非线性规划问题（不用ga函数）

for i = 1:2:n-1 if rand() < pc crossoverPoint = randi([2, chromLength-1]); offspringPopulation(i, :) = [selectedPopulation(i, 1:crossoverPoint), selectedPopulation(i+1, crossoverPoint+1:end)]; ...

我的bounds定义的为Min_pump_zcjj = 26 Max_pump_zcjj = 51 # Y坐标约束条件 Min_pump_bdljd = 26 Max_pump_bdljd = 51 # 靶点位置约束条件 Min_pump_bdwz = 0 Max_pump_bdwz = 1 # 射孔厚度约束条件 Min_pump_skhd = 1 Max_pump_skhd = 5 bounds = np.array([[Min_pump_zcjj, Max_pump_zcjj], [Min_pump_bdljd, Max_pump_bdljd],[Min_pump_bdwz, Max_pump_bdwz],[Min_pump_skhd, Max_pump_skhd]])，该如何修改

for i in range(100): solutions = optimizer.ask() fitness_values = [fitness(x) for x in solutions] optimizer.tell(solutions, fitness_values) optimizer.disp() 在这个例子中，bounds数组定义了四...

class Model(): def init(self): self.best_sol=None self.demand_dict={} self.vehicle_dict={} self.vehicle_type_list=[] self.demand_id_list=[] self.sol_list=[] self.distance_matrix={} self.number_of_demands=0 self.pc=0.5 self.pm=0.1 self.popsize=100 self.n_select=80 self.opt_type=1 # 翻译一下

这段代码定义了一个类 Model，在初始化函数 __init__() 中定义了该类的属性。这些属性包括： - best_sol: 最优解，默认为 None。 - demand_dict: 需求字典，用于存储...- opt_type：优化类型，默认为 1。

优化代码：def crossSol(model): sol_list=copy.deepcopy(model.sol_list) model.sol_list=[] while True: f1_index = random.randint(0, len(sol_list) - 1) f2_index = random.randint(0, len(sol_list) - 1) if f1_index!=f2_index: f1 = copy.deepcopy(sol_list[f1_index]) f2 = copy.deepcopy(sol_list[f2_index]) if random.random() <= model.pc: cro1_index=int(random.randint(0,len(model.demand_id_list)-1)) cro2_index=int(random.randint(cro1_index,len(model.demand_id_list)-1)) new_c1_f = [] new_c1_m=f1.node_id_list[cro1_index:cro2_index+1] new_c1_b = [] new_c2_f = [] new_c2_m=f2.node_id_list[cro1_index:cro2_index+1] new_c2_b = [] for index in range(len(model.demand_id_list)):#遍历长度 if len(new_c1_f)<cro1_index: if f2.node_id_list[index] not in new_c1_m: new_c1_f.append(f2.node_id_list[index]) else: if f2.node_id_list[index] not in new_c1_m: new_c1_b.append(f2.node_id_list[index]) for index in range(len(model.demand_id_list)): if len(new_c2_f)<cro1_index: if f1.node_id_list[index] not in new_c2_m: new_c2_f.append(f1.node_id_list[index]) else: if f1.node_id_list[index] not in new_c2_m: new_c2_b.append(f1.node_id_list[index]) new_c1=copy.deepcopy(new_c1_f) new_c1.extend(new_c1_m) new_c1.extend(new_c1_b) f1.nodes_seq=new_c1 new_c2=copy.deepcopy(new_c2_f) new_c2.extend(new_c2_m) new_c2.extend(new_c2_b) f2.nodes_seq=new_c2 model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) else: model.sol_list.append(copy.deepcopy(f1)) model.sol_list.append(copy.deepcopy(f2)) if len(model.sol_list)>model.popsize: break

new_c1_m, new_c2_m = f1.node_id_list[cro1_index:cro2_index+1], f2.node_id_list[cro1_index:cro2_index+1] new_c1_f = [node for node in f2.node_id_list[:cro1_index] if node not in new_c1_m] new_c1_b =...

将以下代码转换为python：function newpop=zmutate(pop,popsize,pm1,pm2,fitness1,M,N,Tn0,Tn1,Q,ST0,maxT,t,maxgen,LCR,ECR,MCR,FC,ICR) %M为辅助坑道数量；N为单元数 x=pop(:,1:2M+1);%分段点位置 y=pop(:,2M+2:4M+2);%是否选择该分段点 z=pop(:,4M+3:6M+4);%开挖方向 W=pop(:,6M+5:8M+6);%作业班次 lenx=length(x(1,:)); leny=length(y(1,:)); lenz=length(z(1,:)); lenW=length(W(1,:)); avefit=sum(fitness1)/popsize; worstfit=min(fitness1); % sumy=sum(y); % lenz=sumy+1; % lenW=sumy+1; for i=1:popsize %选择popsize次，每次选择一个，输出一个 %随机选择一个染色体 pick=rand; while pick==0 pick=rand; end index=ceil(pickpopsize); f1=fitness1(index); if f1<=avefit % pm=(exp(-t/maxgen))(pm1-(pm1-pm2)(f1-avefit)/max(fitness1)-avefit); pm=1/(1+exp(t/maxgen))(pm1-(pm1-pm2)(f1-avefit)/max(fitness1)-avefit); else % pm=(exp(-t/maxgen))pm1; pm=1/(1+exp(t/maxgen))pm1; end pick=rand; while pick==0 pick=rand; end if pick>pm continue; end % flag0=0; % while(flag0==0) %随机选择变异位置 pick1=rand; pick2=rand; pick3=rand; pick4=rand; while pick1pick2pick3pick4==0 pick1=rand; pick2=rand; pick3=rand; pick4=rand; end posx=ceil(pick1lenx); posy=ceil(pick2leny); %x,y变异 randx=randi([1,N-1]); while ismember(randx,x(index,:)) randx=randi([1,N-1]); end b=x(index,posx); x(index,posx)=randx; a=[0 1]; c=y(index,posy); y(index,posy)=setxor(y(index,posy),a); %z,W变异 posz=ceil(pick3lenz); posW=ceil(pick4lenW); d=z(index,posz); z(index,posz)=setxor(z(index,posz),a); randW=randi([1,3]); while randW==W(index,posW) randW=randi([1,3]); end e=W(index,posW); W(index,posW)=randW; mpop=[x(index,:),y(index,:),z(index,:),W(index,:)]; mtime=ztime(mpop,M,N,Tn0,Tn1,Q,ST0); mutfit=zcost(mpop,M,N,mtime(:,1),mtime(:,2:2M+3),mtime(:,2M+4:2M+2+N),LCR,ECR,MCR,FC,ICR,Q); if mtime(:,1)>maxT||mutfit<=worstfit x(index,posx)=b; y(index,posy)=c; z(index,posz)=d; W(index,posW)=e; end end newpop=[x,y,z,W]; end

y = pop[:, 2*M+2:4*M+2] # 是否选择该分段点 z = pop[:, 4*M+3:6*M+4] # 开挖方向 W = pop[:, 6*M+5:8*M+6] # 作业班次 lenx = x.shape[1] leny = y.shape[1] lenz = z.shape[1] lenW = W.shape[1] ...

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将以下代码转换为python：for i=1:GSAmaxgen tonefor0=cputime; i for j=1:popsize fitness(j,1)=funt(Q,pr0,GSApop0(j,:),M,NM,BL,N,L,pre,f,AT,l); fitness(j,2)=func(GSApop0(j,:),M,NM,N,LCR); end averfit=sum(fitness(:,1))/popsize; elitefit=min(fitness(:,1)); FIT=[elitefit,averfit];

initial_similarity = 0; for i = 1:PopSize population(i,:)=initial(randperm(length(initial(:)))); end mpopulation = unique(population,'row'); initial_similarity = size(mpopulation,1)/size(population,1);

将以下代码转换为python：for j=1:popsize fitness(j,1)=funt(Q,pr0,GSApop0(j,:),M,NM,BL,N,L,pre,f,AT,l); fitness(j,2)=func(GSApop0(j,:),M,NM,N,LCR);

class Model(): def __init__(self): self.best_sol=None self.demand_dict={} self.vehicle_dict={} self.vehicle_type_list=[] self.demand_id_list=[] self.sol_list=[] self.distance_matrix={} self.number_of_demands=0 self.pc=0.5 self.pm=0.1 self.popsize=100 self.n_select=80 self.opt_type=1 # 翻译一下

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class Model(): def init(self): self.best_sol=None self.demand_dict={} self.vehicle_dict={} self.vehicle_type_list=[] self.demand_id_list=[] self.sol_list=[] self.distance_matrix={} self.number_of_demands=0 self.pc=0.5 self.pm=0.1 self.popsize=100 self.n_select=80 self.opt_type=1 # 翻译一下