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pso_mechanism_parameter_2.py
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pso_mechanism_parameter_2.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Oct 10 16:16:09 2018
1. 0.28410760178591277 3190.0751749837323 767276.0387534217
2. 0.26829530630188514 7808.9310167159065 977426968.1963513
3. 0.23188559015286458 26096.859991932768 12873352844116.08
4. 0.1992779342100583 21922.634487073814 358323205937.04987 5083769249096.261 3426418817.992977
5. 0.19173575451067892 24900.332200952605 2223647839088.7275 2385226739230.9478 24017704.57815525 258980884422.3991
"""
import pandas as pd
from All_data import combustion_time
import matplotlib.pyplot as plt
import numpy as np
import subprocess
from math import sqrt
import os
class PSO:
def __init__(self):
self.w = self.getweight()
self.lr = self.getlearningrate()
self.maxgen = self.getmaxgen()
self.sizepop = self.getsizepop()
self.rangepop_x,self.rangepop_y = self.getrangepop()
self.rangespeed_x,self.rangespeed_y = self.getrangespeed()
def getweight(self):
# 惯性权重
weight = 0.8
return weight
def getlearningrate(self):
# 分别是粒子的个体和社会的学习因子，也称为加速常数
lr = (0.495,1.2)
return lr
def getmaxgen(self):
# 最大迭代次数
maxgen = 30
return maxgen
def getsizepop(self):
# 种群规模
sizepop = 30
return sizepop
def getrangepop(self):
# 粒子的位置的范围限制,x、y.z方向的限制相同 ##x,y、z即为我们要设定的参数A,b,E这三个参数的变化区间5e11,2.15e4
rangepop_x = (-1,1)
rangepop_y = (-1,1)
return rangepop_x,rangepop_y
def getrangespeed(self):
# 粒子的速度范围限制
rangespeed_x = (-0.3,0.3)
rangespeed_y = (-0.3,0.3)
return rangespeed_x,rangespeed_y
def error(self,a,b):
error=[]
for i in range(len(a)):
error.append(abs((a[i]-b[i])/b[i]))
relative_error = sum(error)/len(error)
return relative_error
def Mean_squared_error(self,a,b):
error=[]
for i in range(len(a)):
error.append((a[i]-b[i])*(a[i]-b[i]))
mse = sum(error)/len(error)
rmse = sqrt(mse)
return rmse
def mechanism_computation(self,path):
data1 = pd.read_csv(path+"/CKSoln_solution_no_1.csv")
data2 = pd.read_csv(path+"/CKSoln_solution_no_2.csv")
data3 = pd.read_csv(path+"/CKSoln_solution_no_3.csv")
data4 = pd.read_csv(path+"/CKSoln_solution_no_4.csv")
data5 = pd.read_csv(path+"/CKSoln_solution_no_5.csv")
data6 = pd.read_csv(path+"/CKSoln_solution_no_6.csv")
data7 = pd.read_csv(path+"/CKSoln_solution_no_7.csv")
data8 = pd.read_csv(path+"/CKSoln_solution_no_8.csv")
data9 = pd.read_csv(path+"/CKSoln_solution_no_9.csv")
data10 = pd.read_csv(path+"/CKSoln_solution_no_10.csv")
data11 = pd.read_csv(path+"/CKSoln_solution_no_11.csv")
data12 = pd.read_csv(path+"/CKSoln_solution_no_12.csv")
data13 = pd.read_csv(path+"/CKSoln_solution_no_13.csv")
#求解13个data的着火时间
time_simplified=[]
k1,l = combustion_time(data1,1)
time1 = data1['Time_Soln#1_(sec)'][l]
k1,l = combustion_time(data2,2)
time2 = data2['Time_Soln#2_(sec)'][l]
k1,l = combustion_time(data3,3)
time3 = data3['Time_Soln#3_(sec)'][l]
k1,l = combustion_time(data4,4)
time4 = data4['Time_Soln#4_(sec)'][l]
k1,l = combustion_time(data5,5)
time5 = data5['Time_Soln#5_(sec)'][l]
k1,l = combustion_time(data6,6)
time6 = data6['Time_Soln#6_(sec)'][l]
k1,l = combustion_time(data7,7)
time7 = data7['Time_Soln#7_(sec)'][l]
k1,l = combustion_time(data8,8)
time8 = data8['Time_Soln#8_(sec)'][l]
k1,l = combustion_time(data9,9)
time9 = data9['Time_Soln#9_(sec)'][l]
k1,l = combustion_time(data10,10)
time10 = data10['Time_Soln#10_(sec)'][l]
k1,l = combustion_time(data11,11)
time11 = data11['Time_Soln#11_(sec)'][l]
k1,l = combustion_time(data12,12)
time12 = data12['Time_Soln#12_(sec)'][l]
k1,l = combustion_time(data13,13)
time13 = data13['Time_Soln#13_(sec)'][l]
time_simplified.extend([time1,time2,time3,time4,time5,time6,time7,time8,time9,time10,time11,time12,time13])
return time_simplified
#定义适应度函数并实现种群初始化
def fitness_func(self,data,label=False):
if(label==True):
data["simplified2"] = data["simplified"].map(lambda x: x*1000000)
y=self.Mean_squared_error(list(data["simplified2"]),list(data["detailed"]))
else:
data["simplified2"] = data["simplified"].map(lambda x: x*1000000)
y=self.error(list(data["simplified2"]),list(data["detailed"]))
return y
def init(self,sizepop): #假设每个粒子只有三个未知数（位置）
pop = np.zeros((sizepop,2))
pop_r = np.zeros((sizepop,2))
v = np.zeros((sizepop,2))
fitness = np.zeros(sizepop) #20个粒子，每个粒子都有一定的初始的适应度
data={}
data2={}
data3={}
data4={}
for i in range(sizepop):
#A:5e8--5e11;E:2.15e3--2.15e5
pop[i] = [(np.random.uniform()-0.5)*2*self.rangepop_x[1],(np.random.uniform()-0.5)*2*self.rangepop_y[1]] #保证20个粒子的随机位置仍在[-2，2]之间,三个未知数的变化区间不同
v[i] = [(np.random.uniform()-0.5)*2*self.rangespeed_x[1],(np.random.uniform()-0.5)*2*self.rangespeed_y[1]]
#将参数数据放入IC16_optimized.input文件中
path3 = "C:\\E_Disk\Ansys_software\ANSYS Chemkin Pro 17.0 Release 15151 Win\workplace\C12_simplified"
IC16_file = open(path3+'\\NC12.inp')
lines = IC16_file.readlines()
IC16_file.close()
#pop_r[i][0] = 10000*pop[i][0]+1.0e4
#pop_r[i][1] = 100*pop[i][1]*1.2e7
pop_r[i][0] = 100**pop[i][0]*2.0e12
pop_r[i][1] = 100**pop[i][1]*3.0e12
#pop_r[i][0] = (10**pop[i][0]*1.5e5)/np.exp(-pop_r[i][1]/(1.98718*700))
pop1=str(pop_r[i][0])
pop2=str(pop_r[i][1])
a='O2+NC12H25=NC12H25-OO '+pop1+' 0.0 0.0'
b='NC12H25-OO=NC12-QOOH '+pop2+' 0.0 2.400E+04'
lines[57] = a
lines[60] = b
IC16_newfile = open(path3+'\\C12_optimized.inp','w')
for newline in lines:
IC16_newfile.write(newline)
IC16_newfile.close()
##多目标工况优化：40atm_1.5,40atm_0.5,10atm_0.5,10atm_1.5三种工况的最优值
#对20atm_1.0工况简化机理进行计算
path="C:\E_Disk\Ansys_software\ANSYS Chemkin Pro 17.0 Release 15151 Win\workplace\C12_simplified"
p = subprocess.Popen(path+'\ST.bat',shell=True,stdout=subprocess.PIPE)
out,err = p.communicate()
time_simplified = self.mechanism_computation(path)
#压力40atm,化学当量比1.5
time_detailed_20atm = [3233.921,781.5868,264.7509,148.611,148.5894,204.8114,300.2886,287.9953,189.1959,108.1476,5.94E+01,1.87E+01,6.71E+00]
temperature = [700,750,800,850,900,950,1000,1050,1100,1150,1200,1300,1400]
Ignition_time = {"Temp":temperature,"simplified":time_simplified,"detailed":time_detailed_20atm}
data[i] = pd.DataFrame(Ignition_time)
os.remove(path+"/CKSoln_solution_no_1.csv")
os.remove(path+"/CKSoln_solution_no_2.csv")
os.remove(path+"/CKSoln_solution_no_3.csv")
os.remove(path+"/CKSoln_solution_no_4.csv")
os.remove(path+"/CKSoln_solution_no_5.csv")
os.remove(path+"/CKSoln_solution_no_6.csv")
os.remove(path+"/CKSoln_solution_no_7.csv")
os.remove(path+"/CKSoln_solution_no_8.csv")
os.remove(path+"/CKSoln_solution_no_9.csv")
os.remove(path+"/CKSoln_solution_no_10.csv")
os.remove(path+"/CKSoln_solution_no_11.csv")
os.remove(path+"/CKSoln_solution_no_12.csv")
os.remove(path+"/CKSoln_solution_no_13.csv")
#
#对40atm_0.5简化工况进行计算
path="C:\E_Disk\Ansys_software\ANSYS Chemkin Pro 17.0 Release 15151 Win\workplace\C12_simplified"
p = subprocess.Popen(path+'\ST2.bat',shell=True,stdout=subprocess.PIPE)
out,err = p.communicate()
time_simplified = self.mechanism_computation(path)
time_detailed_40atm = [4133.79,1219.066,604.7849,489.5824,513.6832,565.1864,666.0549,555.5153,348.3442,195.5727,1.06E+02,3.14E+01,1.01E+01]
temperature = [700,750,800,850,900,950,1000,1050,1100,1150,1200,1300,1400]
Ignition_time = {"Temp":temperature,"simplified":time_simplified,"detailed":time_detailed_40atm}
data2[i] = pd.DataFrame(Ignition_time)
os.remove(path+"/CKSoln_solution_no_1.csv")
os.remove(path+"/CKSoln_solution_no_2.csv")
os.remove(path+"/CKSoln_solution_no_3.csv")
os.remove(path+"/CKSoln_solution_no_4.csv")
os.remove(path+"/CKSoln_solution_no_5.csv")
os.remove(path+"/CKSoln_solution_no_6.csv")
os.remove(path+"/CKSoln_solution_no_7.csv")
os.remove(path+"/CKSoln_solution_no_8.csv")
os.remove(path+"/CKSoln_solution_no_9.csv")
os.remove(path+"/CKSoln_solution_no_10.csv")
os.remove(path+"/CKSoln_solution_no_11.csv")
os.remove(path+"/CKSoln_solution_no_12.csv")
os.remove(path+"/CKSoln_solution_no_13.csv")
#
#对8atm_0.5简化工况进行计算
path="C:\E_Disk\Ansys_software\ANSYS Chemkin Pro 17.0 Release 15151 Win\workplace\C12_simplified"
p = subprocess.Popen(path+'\ST3.bat',shell=True,stdout=subprocess.PIPE)
out,err = p.communicate()
time_simplified = self.mechanism_computation(path)
time_detailed_10atm = [8907.39,7368.639,10401.59,14841.4,20517.01,14481.84,7262.785,3296.223,1466.022,667.4111,314.1158,7.59E+01,2.24E+01]
temperature = [700,750,800,850,900,950,1000,1050,1100,1150,1200,1300,1400]
Ignition_time = {"Temp":temperature,"simplified":time_simplified,"detailed":time_detailed_10atm}
data3[i] = pd.DataFrame(Ignition_time)
os.remove(path+"/CKSoln_solution_no_1.csv")
os.remove(path+"/CKSoln_solution_no_2.csv")
os.remove(path+"/CKSoln_solution_no_3.csv")
os.remove(path+"/CKSoln_solution_no_4.csv")
os.remove(path+"/CKSoln_solution_no_5.csv")
os.remove(path+"/CKSoln_solution_no_6.csv")
os.remove(path+"/CKSoln_solution_no_7.csv")
os.remove(path+"/CKSoln_solution_no_8.csv")
os.remove(path+"/CKSoln_solution_no_9.csv")
os.remove(path+"/CKSoln_solution_no_10.csv")
os.remove(path+"/CKSoln_solution_no_11.csv")
os.remove(path+"/CKSoln_solution_no_12.csv")
os.remove(path+"/CKSoln_solution_no_13.csv")
#对8atm_1.5简化工况进行计算
path="C:\E_Disk\Ansys_software\ANSYS Chemkin Pro 17.0 Release 15151 Win\workplace\C12_simplified"
p = subprocess.Popen(path+'\ST4.bat',shell=True,stdout=subprocess.PIPE)
out,err = p.communicate()
time_simplified = self.mechanism_computation(path)
time_detailed_10atm = [4722.957,2073.3,2587.254,5247.049,9606.955,7841.339,4133.981,1945.242,904.589,432.6391,215.2988,6.16E+01,2.26E+01]
temperature = [700,750,800,850,900,950,1000,1050,1100,1150,1200,1300,1400]
Ignition_time = {"Temp":temperature,"simplified":time_simplified,"detailed":time_detailed_10atm}
data4[i] = pd.DataFrame(Ignition_time)
os.remove(path+"/CKSoln_solution_no_1.csv")
os.remove(path+"/CKSoln_solution_no_2.csv")
os.remove(path+"/CKSoln_solution_no_3.csv")
os.remove(path+"/CKSoln_solution_no_4.csv")
os.remove(path+"/CKSoln_solution_no_5.csv")
os.remove(path+"/CKSoln_solution_no_6.csv")
os.remove(path+"/CKSoln_solution_no_7.csv")
os.remove(path+"/CKSoln_solution_no_8.csv")
os.remove(path+"/CKSoln_solution_no_9.csv")
os.remove(path+"/CKSoln_solution_no_10.csv")
os.remove(path+"/CKSoln_solution_no_11.csv")
os.remove(path+"/CKSoln_solution_no_12.csv")
os.remove(path+"/CKSoln_solution_no_13.csv")
print("第%d个粒子初始化数据完成." %(i))
fitness[i] = (self.fitness_func(data[i]) + self.fitness_func(data2[i]) + self.fitness_func(data3[i])+self.fitness_func(data4[i]))/4 #得到30个初始化粒子的简化机理着火时间数据后，与详细机理的着火时间数据进行比较，得到适应度
#fitness[i] = self.fitness_func(data[i])
return pop,v,fitness
#寻找初始化后的极值
def getinitbest(self,fitness,pop):
#群体最优的粒子位置和适应度值;;寻找最小值，使得适应度函数最小
gbestpop,gbestfitness = pop[fitness.argmin()].copy(),fitness.min()
#个体最优的粒子位置及其适应度值
pbestpop,pbestfitness = pop.copy(),fitness.copy()
return gbestpop,gbestfitness,pbestpop,pbestfitness
#迭代寻优
def run(self):
pop,v,fitness = self.init(self.sizepop)
gbestpop,gbestfitness,pbestpop,pbestfitness = self.getinitbest(fitness,pop)
pop_r = np.zeros((self.sizepop,2))
result = np.zeros(self.maxgen)
data={}
data2={}
data3 = {}
data4={}
for i in range(self.maxgen):
#速度更新
for j in range(self.sizepop):
v[j] =v[j]*self.w + self.lr[0]*np.random.rand()*(pbestpop[j]-pop[j])+self.lr[1]*np.random.rand()*(gbestpop-pop[j])##不使用固定权重，加了一个[0,1]之间随机变化的权重
if v[j][0]<self.rangespeed_x[0]:
v[j][0] = self.rangespeed_x[0]
if v[j][1]<self.rangespeed_y[0]:
v[j][1] = self.rangespeed_y[0]
if v[j][0]>self.rangespeed_x[1]:
v[j][0] = self.rangespeed_x[1]
if v[j][1]>self.rangespeed_y[1]:
v[j][1] = self.rangespeed_y[1]
#v[v[j][0]<self.rangespeed_x[0]*2.5e11] = self.rangespeed_x[0]
#v[v[j][1]<self.rangespeed_y[0]*3.5e3] = self.rangespeed_y[0]
#v[v[j][0]>self.rangespeed_x[1]*2.5e11] = self.rangespeed_x[1]
#v[v[j][1]>self.rangespeed_y[1]*3.5e3] = self.rangespeed_y[1]
#位置更新
for j in range(self.sizepop):
pop[j] += v[j]
if pop[j][0]<self.rangepop_x[0]:
pop[j][0] = self.rangepop_x[0]
if pop[j][1]<self.rangepop_y[0]:
pop[j][1] = self.rangepop_y[0]
if pop[j][0]>self.rangepop_x[1]:
pop[j][0] = self.rangepop_x[1]
if pop[j][1]>self.rangepop_y[1]:
pop[j][1] = self.rangepop_x[1]
#适应度更新
#将参数数据放入IC16_optimized.input文件中
for j in range(self.sizepop):
path3 = "C:\\E_Disk\Ansys_software\ANSYS Chemkin Pro 17.0 Release 15151 Win\workplace\C12_simplified"
IC16_file = open(path3+'\\NC12.inp')
lines = IC16_file.readlines()
IC16_file.close()
pop_r[j][0] = 100**pop[j][0]*2.0e12
pop_r[j][1] = 100**pop[j][1]*3.0e12
#pop_r[i][0] = (10**pop[i][0]*1.5e5)/np.exp(-pop_r[i][1]/(1.98718*700))
pop1=str(pop_r[j][0])
pop2=str(pop_r[j][1])
a='O2+NC12H25=NC12H25-OO '+pop1+' 0.0 0.0'
b='NC12H25-OO=NC12-QOOH '+pop2+' 0.0 2.400E+04'
lines[57] = a
lines[60] = b
IC16_newfile = open(path3+'\\C12_optimized.inp','w')
for newline in lines:
IC16_newfile.write(newline)
IC16_newfile.close()
# #对40atm_1.5简化机理进行计算
path="C:\E_Disk\Ansys_software\ANSYS Chemkin Pro 17.0 Release 15151 Win\workplace\C12_simplified"
p = subprocess.Popen(path+'\ST.bat',shell=True,stdout=subprocess.PIPE)
out,err = p.communicate()
time_simplified = self.mechanism_computation(path)
# #压力40atm,化学当量比1.5
time_detailed_20atm = [3233.921,781.5868,264.7509,148.611,148.5894,204.8114,300.2886,287.9953,189.1959,108.1476,5.94E+01,1.87E+01,6.71E+00]
temperature = [700,750,800,850,900,950,1000,1050,1100,1150,1200,1300,1400]
Ignition_time = {"Temp":temperature,"simplified":time_simplified,"detailed":time_detailed_20atm}
data[j] = pd.DataFrame(Ignition_time)
os.remove(path+"/CKSoln_solution_no_1.csv")
os.remove(path+"/CKSoln_solution_no_2.csv")
os.remove(path+"/CKSoln_solution_no_3.csv")
os.remove(path+"/CKSoln_solution_no_4.csv")
os.remove(path+"/CKSoln_solution_no_5.csv")
os.remove(path+"/CKSoln_solution_no_6.csv")
os.remove(path+"/CKSoln_solution_no_7.csv")
os.remove(path+"/CKSoln_solution_no_8.csv")
os.remove(path+"/CKSoln_solution_no_9.csv")
os.remove(path+"/CKSoln_solution_no_10.csv")
os.remove(path+"/CKSoln_solution_no_11.csv")
os.remove(path+"/CKSoln_solution_no_12.csv")
os.remove(path+"/CKSoln_solution_no_13.csv")
#
#对40atm_0.5简化工况进行计算
path="C:\E_Disk\Ansys_software\ANSYS Chemkin Pro 17.0 Release 15151 Win\workplace\C12_simplified"
p = subprocess.Popen(path+'\ST2.bat',shell=True,stdout=subprocess.PIPE)
out,err = p.communicate()
time_simplified = self.mechanism_computation(path)
time_detailed_40atm = [4133.79,1219.066,604.7849,489.5824,513.6832,565.1864,666.0549,555.5153,348.3442,195.5727,1.06E+02,3.14E+01,1.01E+01]
temperature = [700,750,800,850,900,950,1000,1050,1100,1150,1200,1300,1400]
Ignition_time = {"Temp":temperature,"simplified":time_simplified,"detailed":time_detailed_40atm}
data2[j] = pd.DataFrame(Ignition_time)
os.remove(path+"/CKSoln_solution_no_1.csv")
os.remove(path+"/CKSoln_solution_no_2.csv")
os.remove(path+"/CKSoln_solution_no_3.csv")
os.remove(path+"/CKSoln_solution_no_4.csv")
os.remove(path+"/CKSoln_solution_no_5.csv")
os.remove(path+"/CKSoln_solution_no_6.csv")
os.remove(path+"/CKSoln_solution_no_7.csv")
os.remove(path+"/CKSoln_solution_no_8.csv")
os.remove(path+"/CKSoln_solution_no_9.csv")
os.remove(path+"/CKSoln_solution_no_10.csv")
os.remove(path+"/CKSoln_solution_no_11.csv")
os.remove(path+"/CKSoln_solution_no_12.csv")
os.remove(path+"/CKSoln_solution_no_13.csv")
#
#对8atm_0.5简化工况进行计算
path="C:\E_Disk\Ansys_software\ANSYS Chemkin Pro 17.0 Release 15151 Win\workplace\C12_simplified"
p = subprocess.Popen(path+'\ST3.bat',shell=True,stdout=subprocess.PIPE)
out,err = p.communicate()
time_simplified = self.mechanism_computation(path)
time_detailed_10atm = [8907.39,7368.639,10401.59,14841.4,20517.01,14481.84,7262.785,3296.223,1466.022,667.4111,314.1158,7.59E+01,2.24E+01]
temperature = [700,750,800,850,900,950,1000,1050,1100,1150,1200,1300,1400]
Ignition_time = {"Temp":temperature,"simplified":time_simplified,"detailed":time_detailed_10atm}
data3[j] = pd.DataFrame(Ignition_time)
os.remove(path+"/CKSoln_solution_no_1.csv")
os.remove(path+"/CKSoln_solution_no_2.csv")
os.remove(path+"/CKSoln_solution_no_3.csv")
os.remove(path+"/CKSoln_solution_no_4.csv")
os.remove(path+"/CKSoln_solution_no_5.csv")
os.remove(path+"/CKSoln_solution_no_6.csv")
os.remove(path+"/CKSoln_solution_no_7.csv")
os.remove(path+"/CKSoln_solution_no_8.csv")
os.remove(path+"/CKSoln_solution_no_9.csv")
os.remove(path+"/CKSoln_solution_no_10.csv")
os.remove(path+"/CKSoln_solution_no_11.csv")
os.remove(path+"/CKSoln_solution_no_12.csv")
os.remove(path+"/CKSoln_solution_no_13.csv")
#对8atm_1.5简化工况进行计算
path="C:\E_Disk\Ansys_software\ANSYS Chemkin Pro 17.0 Release 15151 Win\workplace\C12_simplified"
p = subprocess.Popen(path+'\ST4.bat',shell=True,stdout=subprocess.PIPE)
out,err = p.communicate()
time_simplified = self.mechanism_computation(path)
time_detailed_10atm = [4722.957,2073.3,2587.254,5247.049,9606.955,7841.339,4133.981,1945.242,904.589,432.6391,215.2988,6.16E+01,2.26E+01]
temperature = [700,750,800,850,900,950,1000,1050,1100,1150,1200,1300,1400]
Ignition_time = {"Temp":temperature,"simplified":time_simplified,"detailed":time_detailed_10atm}
data4[j] = pd.DataFrame(Ignition_time)
os.remove(path+"/CKSoln_solution_no_1.csv")
os.remove(path+"/CKSoln_solution_no_2.csv")
os.remove(path+"/CKSoln_solution_no_3.csv")
os.remove(path+"/CKSoln_solution_no_4.csv")
os.remove(path+"/CKSoln_solution_no_5.csv")
os.remove(path+"/CKSoln_solution_no_6.csv")
os.remove(path+"/CKSoln_solution_no_7.csv")
os.remove(path+"/CKSoln_solution_no_8.csv")
os.remove(path+"/CKSoln_solution_no_9.csv")
os.remove(path+"/CKSoln_solution_no_10.csv")
os.remove(path+"/CKSoln_solution_no_11.csv")
os.remove(path+"/CKSoln_solution_no_12.csv")
os.remove(path+"/CKSoln_solution_no_13.csv")
print('第%d次迭代第%d个粒子更新数据完成.' % (i+1,j))
fitness[j] = (self.fitness_func(data[j]) + self.fitness_func(data2[j]) + self.fitness_func(data3[j])+ self.fitness_func(data4[j]))/4
#fitness[j] = self.fitness_func(data[j])
for j in range(self.sizepop):
if fitness[j]<pbestfitness[j]:
pbestfitness[j] = fitness[j]
pbestpop[j] = pop[j].copy()
if pbestfitness.min()<gbestfitness:
gbestfitness = pbestfitness.min()
gbestpop = pop[pbestfitness.argmin()].copy()
print(gbestfitness,(100**gbestpop[0]*2.0e12),(100**gbestpop[1]*3.0e12))
result[i]= gbestfitness
return result
pso = PSO()
result = pso.run()
#%%
plt.figure()
plt.plot(result)
plt.show()