HFSP_GA.zip

import random import numpy as np import copy from Scheduling import Scheduling as Sch from Instance import Job,State,Machine,PT import matplotlib.pyplot as plt class GA: def __init__(self,J_num,State,Machine,PT): self.State=State self.Machine=Machine self.PT=PT self.J_num=J_num self.Pm=0.2 self.Pc=0.9 self.Pop_size=100 # 随机产生染色体 def RCH(self): Chromo = [i for i in range(self.J_num)] random.shuffle(Chromo) return Chromo # 生成初始种群 def CHS(self): CHS = [] for i in range(self.Pop_size): CHS.append(self.RCH()) return CHS #选择 def Select(self, Fit_value): Fit = [] for i in range(len(Fit_value)): fit = 1 / Fit_value[i] Fit.append(fit) Fit = np.array(Fit) idx = np.random.choice(np.arange(len(Fit_value)), size=len(Fit_value), replace=True, p=(Fit) / (Fit.sum())) return idx # 交叉 def Crossover(self, CHS1, CHS2): T_r = [j for j in range(self.J_num)] r = random.randint(2, self.J_num) # 在区间[1,T0]内产生一个整数r random.shuffle(T_r) R = T_r[0:r] # 按照随机数r产生r个互不相等的整数 # 将父代的染色体复制到子代中去，保持他们的顺序和位置 H1=[CHS1[_] for _ in R] H2=[CHS2[_] for _ in R] C1=[_ for _ in CHS1 if _ not in H2] C2=[_ for _ in CHS2 if _ not in H1] CHS1,CHS2=[],[] k,m=0,0 for i in range(self.J_num): if i not in R: CHS1.append(C1[k]) CHS2.append(C2[k]) k+=1 else: CHS1.append(H2[m]) CHS2.append(H1[m]) m+=1 return CHS1, CHS2 # 变异 def Mutation(self, CHS): Tr = [i_num for i_num in range(self.J_num)] # 机器选择部分 r = random.randint(1, self.J_num) # 在变异染色体中选择r个位置 random.shuffle(Tr) T_r = Tr[0:r] K=[] for i in T_r: K.append(CHS[i]) random.shuffle(K) k=0 for i in T_r: CHS[i]=K[k] k+=1 return CHS def main(self): BF=[] x=[_ for _ in range(self.Pop_size+1)] C=self.CHS() Fit=[] for C_i in C: s=Sch(self.J_num,self.Machine,self.State,self.PT) s.Decode(C_i) Fit.append(s.fitness) best_C = None best_fit=min(Fit) BF.append(best_fit) for i in range(self.Pop_size): C_id=self.Select(Fit) C=[C[_] for _ in C_id] for Ci in range(len(C)): if random.random()<self.Pc: _C=[C[Ci]] CHS1,CHS2=self.Crossover(C[Ci],random.choice(C)) _C.extend([CHS1,CHS2]) Fi=[] for ic in _C: s = Sch(self.J_num, self.Machine, self.State, self.PT) s.Decode(ic) Fi.append(s.fitness) C[Ci]=_C[Fi.index(min(Fi))] Fit.append(min(Fi)) elif random.random()<self.Pm: CHS1=self.Mutation(C[Ci]) C[Ci]=CHS1 Fit = [] Sc=[] for C_i in C: s = Sch(self.J_num, self.Machine, self.State, self.PT) s.Decode(C_i) Sc.append(s) Fit.append(s.fitness) if min(Fit)<best_fit: best_fit=min(Fit) best_C=Sc[Fit.index(min(Fit))] BF.append(best_fit) plt.plot(x,BF) plt.show() best_C.Gantt() if __name__=="__main__": g=GA(Job,State,Machine,PT) g.main()