- Develpoment Environment: Windows 64, Visual Studio 2015: Visual Studio 2015 Project
-
This shared Library is based on the NSGA-II (Non-dominated Sorting Genetic Algorithm) riginal code in C from:
Dr. Kalyanmoy Deb: http://www.iitk.ac.in/kangal/
-
This shared Library is based our NSGA-II shared Library for developing the applications
around Environmental-Economic Electric Power Dispatch
-
Python 3.5
-
Python packages： Numpy Matplotlib Tornado
-
The standard IEEE six-generator 30-bus test system
- M.A. Abido. A novel multiobjective evolutionary algorithm for environmental/economic power dispatch, Electric Power Systems Research. (65):71-81 , 2003,）
is used to demonstrate the effectiveness of our shared Library
2.1 Example of API with Python: PythonExample
- Run
>python biobjloaddispatch.pyfrom ctypes import *
import numpy as np
import pylab as plt
class nsga2cfg(Structure):
_fields_ = [("nreal",c_int),
("nobj",c_int),
("ncon",c_int),
("popsize",c_int),
("ngen",c_int),
("pcross_real",c_double),
("pmut_real",c_double),
("eta_c",c_double),
("eta_m",c_double)]
class curvecoff(Structure):
_fields_ = [("c",c_float*3),
("e",c_float*5)]
class unitload(Structure):
_fields_ = [("min",c_float),
("max",c_float),
("coff",curvecoff)]
class loadv(Structure):
_fields_ = [("xreal",c_float*10),
("cobj",c_float),
("eobj",c_float)]
load_min =[0.05,0.05,0.05,0.05,0.05,0.05]
load_max = (c_double*6)()
load_max =[1.5,1.5,1.15,1.5,1.5,1.5]
c=[[10,200,100],
[10,150,120],
[20,180,40],
[10,100,60],
[20,180,40],
[10,150,100]]
e = (c_double*6*5)()
e=[[4.091,-5.554,6.490,2.0e-4,2.857],
[2.543,-6.047,5.638,5.0e-4,3.333],
[4.258,-5.094,4.586,1.0e-6,8.000],
[5.426,-3.550,3.380,2.0e-3,2.000],
[4.258,-5.094,4.586,1.0e-6,8.000],
[6.131,-5.555,5.151,1.0e-5,6.667]]
mydll=windll.libseu_biobjloaddispatch
f=mydll.seu_biobjloaddispatch
unitnum=c_int()
totalload=c_double()
ga=nsga2cfg()
popsize=c_int()
bestC=loadv()
bestE=loadv()
best=loadv()
unitnum=6
uload=(unitload*unitnum)()
for i in range(unitnum):
uload[i].min = load_min[i]
uload[i].max = load_max[i]
for j in range(3):
uload[i].coff.c[j]=c[i][j]
for j in range(5):
uload[i].coff.e[j]=e[i][j]
ga.nreal=unitnum
ga.nobj=2
ga.ncon=1
ga.popsize=200
ga.ngen=200
ga.pcross_real=0.9
ga.pmut_real=0.1
ga.eta_c=20
ga.eta_m=15
x = (POINTER(c_double) * ga.popsize)()
for i in range(ga.popsize):
x[i] = (c_double * ga.nreal)()
obj = (POINTER(c_double) * ga.popsize)()
for i in range(ga.popsize):
obj[i] = (c_double *ga.nobj)()
totalload=2.834
f(ga,unitnum,uload,c_double(totalload),byref(x),byref(obj),byref(popsize),byref(bestC),byref(bestE),byref(best))
print('--bestC--')
print(bestC.cobj)
print(bestC.eobj)
print('--load--')
for i in range(ga.nreal):
print(bestC.xreal[i]*100)
print('--bestE--')
print(bestE.cobj)
print(bestE.eobj)
print('--load--')
for i in range(ga.nreal):
print(bestE.xreal[i]*100)
print('--best--')
print(best.cobj)
print(best.eobj)
print('--load--')
for i in range(ga.nreal):
print(best.xreal[i]*100)
# print('--pareto--cobj')
c=np.zeros(shape=(ga.popsize))
for i in range(ga.popsize):
c[i]=obj[i][0]
# print(obj[i][0])
# print('--pareto--eobj')
e=np.zeros(shape=(ga.popsize))
for i in range(ga.popsize):
e[i]=obj[i][1]
# print(obj[i][1])
bc=np.zeros(shape=(1))
bc[0]=best.cobj
be=np.zeros(shape=(1))
be[0]=best.eobj
plt.plot(c,e,'b+',label='Pareto')
plt.plot(bc,be,'ro',label='Best')
plt.minorticks_on()
plt.xlabel('c($/h)')
plt.ylabel('e(t/h)')
plt.show()
The simple Web application is based Tornado
- Run
>python app.py
3 PPT Chinese
-
This PPT is designed for introduce our shared library and it's application
at Chinese Undergraduate Computer Design Contest 2014
-
1st Prize of 7th China Undergraduate Computer Design Contest in Jiangsu Province
-
3rd Prize of 7th China Undergraduate Computer Design Contest
MIT