-- coding:utf-8 --
@FileName :QPSO.py
@Time :2021/2/25 15:48
@Author :Praise
import pandas as pd
import math
import random
import numpy as np
import copy
import xlsxwriter
import matplotlib.pyplot as plt
class Sol():
def __init__(self):
self.nodes_seq=None
self.obj=None
self.fit=None
self.routes=None
class Node():
def __init__(self):
self.id=0
self.name=''
self.seq_no=0
self.x_coord=0
self.y_coord=0
self.demand=0
class Model():
def __init__(self):
self.best_sol=None
self.node_list=[]
self.sol_list=[]
self.node_seq_no_list=[]
self.depot=None
self.number_of_nodes=0
self.opt_type=0
self.vehicle_cap=0
self.pc=0.5
self.pm=0.2
self.n_select=80
self.popsize=100
def readXlsxFile(filepath,model):
node_seq_no =-1
df = pd.read_excel(filepath)
for i in range(df.shape[0]):
node=Node()
node.id=node_seq_no
node.seq_no=node_seq_no
node.x_coord= df['x_coord'][i]
node.y_coord= df['y_coord'][i]
node.demand=df['demand'][i]
if df['demand'][i] == 0:
model.depot=node
else:
model.node_list.append(node)
model.node_seq_no_list.append(node_seq_no)
try:
node.name=df['name'][i]
except:
pass
try:
node.id=df['id'][i]
except:
pass
node_seq_no=node_seq_no+1
model.number_of_nodes=len(model.node_list)
def genInitialSol(model):
nodes_seq=copy.deepcopy(model.node_seq_no_list)
for i in range(model.popsize):
seed=int(random.randint(0,10))
random.seed(seed)
random.shuffle(nodes_seq)
sol=Sol()
sol.nodes_seq=copy.deepcopy(nodes_seq)
model.sol_list.append(sol)
def splitRoutes(nodes_seq,model):
"""
采用简单的分割方法：按顺序依次检查路径的容量约束，在超出车辆容量限制的位置插入车场。
例如某TSP解为：[1,2,3,4,5,6,7,8,9,10],累计需求为：[10,20,30,40,50,60,70,80,90,10]，车辆容量为：30，则应在3,6,9节点后插入车场，
即得到：[0,1,2,3,0,4,5,6,0,7,8,9,0,10,0]
"""
num_vehicle = 0
vehicle_routes = []
route = []
remained_cap = model.vehicle_cap
for node_no in nodes_seq:
if remained_cap - model.node_list[node_no].demand >= 0:
route.append(node_no)
remained_cap = remained_cap - model.node_list[node_no].demand
else:
vehicle_routes.append(route)
route = [node_no]
num_vehicle = num_vehicle + 1
remained_cap =model.vehicle_cap - model.node_list[node_no].demand
vehicle_routes.append(route)
return num_vehicle,vehicle_routes
def calDistance(route,model):
distance=0
depot=model.depot
for i in range(len(route)-1):
from_node=model.node_list[route[i]]
to_node=model.node_list[route[i+1]]
distance+=math.sqrt((from_node.x_coord-to_node.x_coord)**2+(from_node.y_coord-to_node.y_coord)**2)
first_node=model.node_list[route[0]]
last_node=model.node_list[route[-1]]
distance+=math.sqrt((depot.x_coord-first_node.x_coord)**2+(depot.y_coord-first_node.y_coord)**2)
distance+=math.sqrt((depot.x_coord-last_node.x_coord)**2+(depot.y_coord - last_node.y_coord)**2)
return distance
def calFit(model):
Objmax=-float('inf')
best_sol=Sol()
best_sol.obj=float('inf')
for sol in model.sol_list:
nodes_seq=sol.nodes_seq
num_vehicle, vehicle_routes = splitRoutes(nodes_seq, model)
if model.opt_type==0:
sol.obj=num_vehicle
sol.routes=vehicle_routes
if sol.obj>Objmax:
Objmax=sol.obj
if sol.obj<best_sol.obj:
best_sol=copy.deepcopy(sol)
else:
distance=0
for route in vehicle_routes:
distance+=calDistance(route,model)
sol.obj=distance
sol.routes=vehicle_routes
if sol.obj>Objmax:
Objmax=sol.obj
if sol.obj < best_sol.obj:
best_sol = copy.deepcopy(sol)
for sol in model.sol_list:
sol.fit=Objmax-sol.obj
if best_sol.obj<model.best_sol.obj:
model.best_sol=best_sol
def selectSol(model):
sol_list=copy.deepcopy(model.sol_list)
model.sol_list=[]
for i in range(model.n_select):
f1_index=random.randint(0,len(sol_list)-1)
f2_index=random.randint(0,len(sol_list)-1)
f1_fit=sol_list[f1_index].fit
f2_fit=sol_list[f2_index].fit
if f1_fit<f2_fit:
model.sol_list.append(sol_list[f2_index])
else:
model.sol_list.append(sol_list[f1_index])
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,model.number_of_nodes-1))
cro2_index=int(random.randint(cro1_index,model.number_of_nodes-1))
new_c1_f = []
new_c1_m=f1.nodes_seq[cro1_index:cro2_index+1]
new_c1_b = []
new_c2_f = []
new_c2_m=f2.nodes_seq[cro1_index:cro2_index+1]
new_c2_b = []
for index in range(model.number_of_nodes):
if len(new_c1_f)<cro1_index:
if f2.nodes_seq[index] not in new_c1_m:
new_c1_f.append(f2.nodes_seq[index])
else:
if f2.nodes_seq[index] not in new_c1_m:
new_c1_b.append(f2.nodes_seq[index])
for index in range(model.number_of_nodes):
if len(new_c2_f)<cro1_index:
if f1.nodes_seq[index] not in new_c2_m:
new_c2_f.append(f1.nodes_seq[index])
else:
if f1.nodes_seq[index] not in new_c2_m:
new_c2_b.append(f1.nodes_seq[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
def muSol(model):
sol_list=copy.deepcopy(model.sol_list)
model.sol_list=[]
while True:
f1_index = int(random.randint(0, len(sol_list) - 1))
f1 = copy.deepcopy(sol_list[f1_index])
m1_index=random.randint(0,model.number_of_nodes-1)
m2_index=random.randint(0,model.number_of_nodes-1)
if m1_index!=m2_index:
if random.random() <= model.pm:
node1=f1.nodes_seq[m1_index]
f1.nodes_seq[m1_index]=f1.nodes_seq[m2_index]
f1.nodes_seq[m2_index]=node1
model.sol_list.append(copy.deepcopy(f1))
else:
model.sol_list.append(copy.deepcopy(f1))
if len(model.sol_list)>model.popsize:
break
def plotObj(obj_list):
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.plot(np.arange(1,len(obj_list)+1),obj_list)
plt.xlabel('Iterations')
plt.ylabel('Obj Value')
plt.grid()
plt.xlim(1,len(obj_list)+1)
plt.show()
def plotRoutes(model):
for route in model.best_sol.routes:
x_coord = [model.depot.x_coord]
y_coord = [model.depot.y_coord]
for node_no in route:
x_coord.append(model.node_list[node_no].x_coord)
y_coord.append(model.node_list[node_no].y_coord)
x_coord.append(model.depot.x_coord)
y_coord.append(model.depot.y_coord)
plt.plot(x_coord, y_coord, marker='s', color='b', linewidth=0.5)
plt.show()
def outPut(model):
work=xlsxwriter.Workbook('result.xlsx')
worksheet=work.add_worksheet()
worksheet.write(0,0,'opt_type')
worksheet.write(1,0,'obj')
if model.opt_type==0:
worksheet.write(0,1,'number of vehicles')
else:
worksheet.write(0, 1, 'drive distance of vehicles')
worksheet.write(1,1,model.best_sol.obj)
for row,route in enumerate(model.best_sol.routes):
route.insert(0,model.depot.id)
route.append(model.depot.id)
worksheet.write(row+2,0,'v'+str(row+1))
r=[str(i)for i in route]
worksheet.write(row+2,1, '-'.join(r))
work.close()
def run(filepath,epochs,pc,pm,popsize,n_select,v_cap,opt_type):
"""
:param filepath: Xlsx文件路径
:param epochs: 迭代次数
:param pc: 交叉概率
:param pm: 变异概率
:param popsize: 种群规模
:param n_select: 优秀父代保留数量
:param v_cap: 车辆容量
:param opt_type: 优化目标，0：最小化车辆数，1：最小化总行驶距离
:return:
"""
model=Model()
model.vehicle_cap=v_cap
model.opt_type=opt_type
model.pc=pc
model.pm=pm
model.popsize=popsize
model.n_select=n_select
readXlsxFile(filepath,model)
genInitialSol(model)
history_best_obj = []
best_sol=Sol()
best_sol.obj=float('inf')
model.best_sol=best_sol
for ep in range(epochs):
calFit(model)
selectSol(model)
crossSol(model)
muSol(model)
history_best_obj.append(model.best_sol.obj)
print("%s/%s， best obj: %s" % (ep,epochs,model.best_sol.obj))
plotObj(history_best_obj)
plotRoutes(model)
outPut(model)
if __name__=='__main__':
file=r'./cvrp.xlsx'
run(filepath=file,epochs=300,pc=0.6,pm=0.2,popsize=100,n_select=80,v_cap=80,opt_type=1)
