- 遗传算法本文应用的是 python geatpy module
- 粒子群算法本文应用的是 python pyswarm module
遗传算法
import geatpy as ea
import numpy as np@ea.Problem.single
def evalVars(Vars): x1 = Vars[0]x2 = Vars[1]x3 = Vars[2]x4 = Vars[3]f = (x1 + 2)**2 +\(x2 - 3)**2 +\(x3 + 2)**2 +\x4CV = np.array([ - x2 + x1 ,-0.001 + (x4-3)**2]) return f, CVproblem = ea.Problem(name='test',M=1, maxormins=[1], Dim=4, varTypes=[0, 0, 0, 0],lb=[-5, -5, -5, -5],ub=[ 5, 5, 5, 5],evalVars=evalVars)algorithm = ea.soea_SEGA_templet(problem,ea.Population(Encoding='RI', NIND=20),MAXGEN=500, logTras=1, trappedValue=1e-8, maxTrappedCount=10) res = ea.optimize(algorithm, seed=1, verbose=True, \drawing=1, outputMsg=True, drawLog=True, \saveFlag=True, dirName='result')
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gen| eval | f_opt | f_max | f_avg | f_min | f_std
----------------------------------------------------------------------------------34| 700 | 1.02093E+01 | 1.02093E+01 | 1.02093E+01 | 1.02093E+01 | 0.00000E+00 35| 720 | 1.02093E+01 | 1.02093E+01 | 1.02093E+01 | 1.02093E+01 | 0.00000E+00 36| 740 | 1.01760E+01 | 2.07075E+01 | 1.15175E+01 | 1.01760E+01 | 3.47351E+00 37| 760 | 1.01760E+01 | 2.07636E+01 | 1.24590E+01 | 1.01760E+01 | 3.94129E+00 38| 780 | 3.07598E+00 | 1.02096E+01 | 9.84393E+00 | 3.07598E+00 | 1.55274E+00 39| 800 | 3.07497E+00 | 1.02093E+01 | 8.79644E+00 | 3.07497E+00 | 2.70962E+00 40| 820 | 3.07497E+00 | 1.01723E+01 | 6.33722E+00 | 3.07497E+00 | 3.20984E+00 41| 840 | 3.05616E+00 | 3.72432E+00 | 3.30975E+00 | 3.05616E+00 | 2.94966E-01 42| 860 | 3.03355E+00 | 3.07598E+00 | 3.07030E+00 | 3.03355E+00 | 1.05715E-02 43| 880 | 3.03355E+00 | 3.07497E+00 | 3.06473E+00 | 3.03355E+00 | 1.05250E-02 44| 900 | 3.03354E+00 | 3.06010E+00 | 3.05215E+00 | 3.03354E+00 | 9.52747E-03 45| 920 | 3.03236E+00 | 3.05616E+00 | 3.04503E+00 | 3.03236E+00 | 1.05466E-02 46| 940 | 3.03236E+00 | 3.05163E+00 | 3.03547E+00 | 3.03236E+00 | 5.48001E-03 47| 960 | 3.03078E+00 | 3.03355E+00 | 3.03308E+00 | 3.03078E+00 | 7.69107E-04 48| 980 | 3.03078E+00 | 3.03354E+00 | 3.03243E+00 | 3.03078E+00 | 8.50674E-04 49| 1000 | 3.03060E+00 | 3.03236E+00 | 3.03177E+00 | 3.03060E+00 | 6.64341E-04 50| 1020 | 3.03060E+00 | 3.03187E+00 | 3.03109E+00 | 3.03060E+00 | 5.18200E-04 51| 1040 | 3.03034E+00 | 3.03080E+00 | 3.03069E+00 | 3.03034E+00 | 1.24733E-04 52| 1060 | 3.02960E+00 | 3.03074E+00 | 3.03050E+00 | 3.02960E+00 | 3.41333E-04 53| 1080 | 3.02931E+00 | 3.03060E+00 | 3.03028E+00 | 3.02931E+00 | 4.18144E-04 54| 1100 | 3.02931E+00 | 3.03056E+00 | 3.02998E+00 | 3.02931E+00 | 4.08041E-04 55| 1120 | 3.02931E+00 | 3.03001E+00 | 3.02960E+00 | 3.02931E+00 | 2.06836E-04 56| 1140 | 3.02910E+00 | 3.02960E+00 | 3.02942E+00 | 3.02910E+00 | 1.40018E-04 57| 1160 | 3.00978E+00 | 3.02951E+00 | 3.02829E+00 | 3.00978E+00 | 4.24883E-03 58| 1180 | 3.00978E+00 | 3.02931E+00 | 3.02719E+00 | 3.00978E+00 | 5.80743E-03 59| 1200 | 3.00978E+00 | 3.02910E+00 | 3.02684E+00 | 3.00978E+00 | 5.71712E-03 60| 1220 | 3.00977E+00 | 3.02876E+00 | 3.02437E+00 | 3.00977E+00 | 7.36375E-03 61| 1240 | 3.00913E+00 | 3.02828E+00 | 3.01725E+00 | 3.00913E+00 | 8.26062E-03 62| 1260 | 3.00913E+00 | 3.01029E+00 | 3.00973E+00 | 3.00913E+00 | 2.63957E-04
Execution time: 0.03466796875 s
Evaluation number: 1260
The best objective value is: 3.009134825271758
The best variables are:
-1.9990825653076172 3.0016613006591797 -2.030458450317383 3.0082035064697266
粒子群算法
from pyswarm import pso
import numpy as npdef opt(x):x1 = x[0]x2 = x[1]x3 = x[2]x4 = x[3]return (x1 + 2)**2 +\(x2 - 3)**2 +\(x3 + 2)**2 +\x4def con(x):x1 = x[0]x2 = x[1]x3 = x[2]x4 = x[3]return [ x2 - x1 ,0.001-(x4-3)**2]lb = [-5, -5, -5, -5]
ub = [ 5, 5, 5, 5]xopt, fopt = pso(opt, lb, ub, f_ieqcons=con)
print("xopt:", xopt)
print("fopt:", fopt)
Stopping search: Swarm best objective change less than 1e-08
xopt: [-2.00094176 3.00064191 -1.99431492 2.96837868]
fopt: 2.9684122988614785
