降低这段代码的重复率：def run(demand_file,depot_file,epochs,pc,pm,popsize,n_select,opt_type): model=Model() model.pc=pc model.pm=pm model.popsize=popsize model.n_select=n_select model.opt_type=opt_type readCSVFile(demand_file,depot_file,model) calDistanceMatrix(model) generateInitialSol(model) history_best_obj = [] best_sol=Sol() best_sol.obj=float('inf') model.best_sol=best_sol start_time=time.time() for ep in range(epochs): ''' 计算适应度''' calFitness(model) ''' 选择''' selectSol(model) ''' 交叉''' crossSol(model) ''' 变异''' muSol(model) ''' 记录最优目标''' history_best_obj.append(model.best_sol.obj) print("迭代次数: %s， 最优目标值: %s， 运行时间: %s" % (ep+1,model.best_sol.obj,time.time()-start_time)) plotObj(history_best_obj) plotRoutes(model) outPut(model)

可以将一些重复的代码抽象成函数或方法，例如：

1. 将读取CSV文件和计算距离矩阵的代码封装成一个方法；
2. 将选择、交叉和变异的代码封装成三个方法。

重构后的代码如下：

def run(demand_file,depot_file,epochs,pc,pm,popsize,n_select,opt_type):
    model=Model()
    model.pc=pc
    model.pm=pm
    model.popsize=popsize
    model.n_select=n_select
    model.opt_type=opt_type
    
    # 读取CSV文件和计算距离矩阵
    initialize(model, demand_file, depot_file)
    
    history_best_obj = []
    best_sol=Sol()
    best_sol.obj=float('inf')
    model.best_sol=best_sol
    start_time=time.time()
    
    for ep in range(epochs):
        # 计算适应度
        calFitness(model)
        # 选择
        selectSol(model)
        # 交叉
        crossSol(model)
        # 变异
        muSol(model)
        # 记录最优目标
        history_best_obj.append(model.best_sol.obj)
        print("迭代次数: %s， 最优目标值: %s， 运行时间: %s" % (ep+1,model.best_sol.obj,time.time()-start_time))
    
    plotObj(history_best_obj)
    plotRoutes(model)
    outPut(model)

def initialize(model, demand_file, depot_file):
    readCSVFile(demand_file, depot_file, model)
    calDistanceMatrix(model)

def selectSol(model):
    ...

def crossSol(model):
    ...

def muSol(model):
    ...