#"""
#BEST 2 - with Swarm2-WOW - WOOOOOOOOOOWWWW
n = 100 #50
r = 19
lamda = 100
f = tf.gaussian_diff_multimodal4_positive
dim = 2
sigma_share = 1 #1.5 #1 Good #0.2 Good #0.6 NOT WORKING
d_min = 1 #0.2 #1
d_max = 3
clust_alpha = 2
step_size = 0.1
#alh = SwarmPackagePy.z_bfoa_multiniche_sharing_v4(n, f, -r, r, dim, 100, 16, 2, 8, 12, step_size, 0.25, 0.05, 0.2, 0.05, 10, lamda, 0.03, 'none' '''adaptive1 also''', 'swarm2', 'false', sigma_share,d_min, d_max, clust_alpha)
alh = SwarmPackagePy.z_bfoa_multiniche_sharing_v4_chi(
n, f, -r, r, dim, 100, 16, 2, 8, 12, step_size, 0.25, 0.05, 0.2, 0.05, 10,
lamda, 0.03, 'none'
'''adaptive1 also''', 'swarm2', 'false', sigma_share, d_min, d_max,
clust_alpha)
trained_centers = alh._get_cluster_centroids()
trained_centers_fit = alh._get_cluster_centroids_fit()
#Chi Test
alh = SwarmPackagePy.z_bfoa_multiniche_sharing_v4_chi_test(
n, f, -r, r, dim, 100, 16, 2, 8, 12, step_size, 0.25, 0.05, 0.2, 0.05, 10,
lamda, 0.03, 'none'
'''adaptive1 also''', 'swarm2', 'false', sigma_share, d_min, d_max,
clust_alpha, 'true', trained_centers, trained_centers_fit)
#"""
#LAST TESTED
'''rastrigin Working , Change last Visualize, LAST one is the BEST, This is EXTRA, Only Change is n=100 not 500
N = 400
#D = 20 #Divisions
M = 2
R = 2
lrm = 4 # for all r,m, Prmk
T = 19
T = T + 1
Nr = [75, 20]
r = max(Nr)
D = 800
Dimention = M * T
dim = Dimention
revf = SwarmPackagePy.revenue_optimization_function(M, R, lrm, T)
#filename = '../../data/data1.csv'
filename = '/home/mahesh/paraqum/repos/SwarmPackagePy/data/data2.csv' #ALl numbers
#revf.read_paramemeters_file(filename)
#read_paramemeters_file(self, filename, Nr, M_=3, R_=2, lrm_=3, T_=2, D = 20 ):
revf.read_paramemeters_file(filename, Nr, 2, 2, 4, 20, 800) #TTotal
print("self.N", revf.N)
print('Press ENTER to continue!')
input()
#No Need
#Set Parameter Commented
#revf.set_parameters(N, M, R , lrm ,T)
#raw_input("Press Enter to continue ...")
#pause()
import SwarmPackagePy
from SwarmPackagePy import testFunctions as tf
from SwarmPackagePy import animation, animation3D
import matplotlib.pyplot as plt
#alh = SwarmPackagePy.abfoa2(20, tf.easom_function, -10, 10, 2, 20, 16, 4, 2, 12, 0.9, 0.25, 0.1)
#animation(alh.get_agents(), tf.easom_function, -10, 10)
#animation3D(alh.get_agents(), tf.easom_function, -10, 10)
#alh = SwarmPackagePy.abfoa2(20, tf.gaussian_function, -10, 10, 2, 20, 16, 4, 2, 12, 0.9, 0.25, 0.1)
#animation(alh.get_agents(), tf.gaussian_function, -10, 10)
#animation3D(alh.get_agents(), tf.gaussian_function, -10, 10)
alh = SwarmPackagePy.abfoa2(100, tf.thriple_gaussian_function_positive, -10, 10, 50, 20, 16, 4, 2, 12, 0.9, 0.25, 1)
steps = alh._get_csteps()
print(steps)
plt.plot(steps)
plt.ylabel('Step Size')
#plt.show()
jlist = alh._get_jlist()
print(jlist)
#plt.plot(jlist,'r')
plt.show()
animation(alh.get_agents(), tf.thriple_gaussian_function_positive, -10, 10)
animation3D(alh.get_agents(), tf.thriple_gaussian_function_positive, -10, 10)
"""
alh = SwarmPackagePy.abfoa2(100, tf.thriple_wide_gaussian_function, -50, 50, 2, 20, 16, 4, 2, 12, 0.9, 0.25, 0.1)
#alh = SwarmPackagePy.z_bfoa(100, f, -r, r, 30, 100, 8, 8, 12, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'adaptive1', 'swarm1', 'false')
#BEST
#alh = SwarmPackagePy.z_bfoa(100, f, -r, r, 30, 100, 8, 8, 12, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 400, 0.03, 'adaptive1', 'swarm1', 'false')
#HARSHA BEST1 DEMO1 Org alh = SwarmPackagePy.z_bfoa(100, f, -r, r, 30, 100, 8, 8, 12, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 400, 0.03, 'adaptive1', 'swarm1', 'false')
#GOOD 1 alh = SwarmPackagePy.z_bfoa(50, f, -r, r, 30, 100, 16, 4, 4, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'adaptive1', 'swarm1', 'false')
#NOT GOOD alh = SwarmPackagePy.z_bfoa(50, f, -r, r, 30, 100, 16, 4, 4, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'adaptive1', 'swarm1', 'true')
#TEST FOR MULTI-NICHE SHARING 2 With testcase Best-2
n = 100 #50
r = 20
lamda = 100
f = tf.gaussian_diff_multimodal_positive
dim = 2
alh = SwarmPackagePy.z_bfoa(100, f, -r, r, 2, 100, 8, 8, 4, 12, 0.1, 0.25, 0.1,
0.2, 0.1, 10, 100, 0.03, 'none', 'swarm2', 'false')
#TEST CASES
########################################################################
#DAMN WORSE - Lets change SOme Parametr Nc = 4
'''
n =100 #50
r = 20
lamda = 100
f = tf.gaussian_diff_multimodal_positive
dim =2
alh = SwarmPackagePy.z_bfoa(100, f, -r, r, 2, 100, 8, 8, 12, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'none', 'swarm1', 'false')
'''
#########################################################################
import SwarmPackagePy
from SwarmPackagePy import testFunctions as tf
from SwarmPackagePy import animation, animation3D
#alh = SwarmPackagePy.pso(50, tf.easom_function, -10, 10, 2, 20,w=0.5, c1=1, c2=1)
#alh = SwarmPackagePy.bfo(n, function, lb, ub, dimension, iteration, Nc, Ns, C, Ped)
alh = SwarmPackagePy.bfo(50, tf.easom_function, -10, 10, 2, 20, 2, 12, 0.2,
1.15)
animation(alh.get_agents(), tf.easom_function, -10, 10)
animation3D(alh.get_agents(), tf.easom_function, -10, 10)
#SwarmPackagePy.bfo(n, function, lb, ub, dimension, iteration, Nc, Ns, C, Ped)
import SwarmPackagePy from SwarmPackagePy import testFunctions as tf from SwarmPackagePy import animation, animation3D eval_function = [ tf.ackley_function, ] """ tf.ackley_function, tf.bukin_function, tf.cross_in_tray_function, tf.sphere_function, tf.bohachevsky_function, tf.sum_squares_function, tf.sum_of_different_powers_function, tf.booth_function, tf.matyas_function, tf.mccormick_function, tf.dixon_price_function, tf.six_hump_camel_function, tf.three_hump_camel_function, tf.easom_function, tf.michalewicz_function, tf.beale_function, tf.drop_wave_function ] """ for func in eval_function: alh = SwarmPackagePy.aba(50, func, -10, 10, 2, 20) animation(alh.get_agents(), func, -10, 10) animation3D(alh.get_agents(), func, -10, 10)
import matplotlib.pyplot as plt
import numpy as np
from math import *
#(self, n, function, lb, ub, dimension, iteration, Nre=16, Ned=4, Nc=2, Ns=12, C=0.1, Ped=0.25, Da=0.1, Wa=0.2, Hr=0.1, Wr=10, lamda=400, L=0.03, arga='none', argj='none', arged='false'):
#alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f1_sphere_function, -100, 100, 3, 100, 8, 4, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,10 )
#alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f5_griewank_function, -10, 10, 30, 100, 16, 4, 2, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,400 )
#VERYGOOD alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f3_ackley_function, -32, 32, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 )
r = 1
#lamda = 100 #FOR MULTINICHE GOOD
lamda = 10
f = tf.F1
alh = SwarmPackagePy.z_bfoa_multiniche_sharing(50, f, -r, r, 1, 100, 8, 8, 12,
12, 0.1, 0.25, 0.1, 0.2, 0.1,
10, lamda, 0.03, 'adaptive1',
'none', 'false')
fits = alh._get_jfits()
plt.plot(fits, 'b', label='J-fit')
jcclist = alh._get_jcclist()
plt.plot(jcclist, 'r', label='J-cc')
jarlist = alh._get_jarlist()
plt.plot(jarlist, 'g', label='J-ar')
jlist = alh._get_jlist()
plt.plot(jlist, 'y', label='J')
jblist = alh._get_jblist()
from SwarmPackagePy import animation, animation3D
import matplotlib.pyplot as plt
#alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f1_sphere_function, -100, 100, 3, 100, 8, 4, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,10 )
#alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f5_griewank_function, -10, 10, 30, 100, 16, 4, 2, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,400 )
#VERYGOOD alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f3_ackley_function, -32, 32, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 )
r = 20
f = tf.gaussian_multimodal3_positive #32 #lamda=50
#f = tf.f3_ackley_function #lamda=100
#f = tf.f2_rosenbrock_function #lamda=400 not enough.
#ACKLEY
#alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f2_rosenbrock_function, -r, r, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 )
#alh = SwarmPackagePy.z_bfoa_swarm1_dev1(100, tf.f3_ackley_function, -32, 32, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 )
# SO FAR BEST alh = SwarmPackagePy.z_ibfoa_jun_li(100, f, -r, r, 30, 100, 8, 8, 16, 4, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100,0.03)
#alh = SwarmPackagePy.z_ibfoa_jun_li_2(200, f, -r, r, 30, 100, 8, 8, 16, 4, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100,0.02)
alh = SwarmPackagePy.z_ibfoa_jun_li_2(100, f, -r, r, 30, 100, 8, 8, 2, 12, 0.1,
0.25, 0.1, 0.2, 0.1, 10, 100, 0.02)
fits = alh._get_jfits()
plt.plot(fits, 'b', label='J-fit')
jcclist = alh._get_jcclist()
plt.plot(jcclist, 'r', label='J-cc')
jarlist = alh._get_jarlist()
plt.plot(jarlist, 'r', label='J-ar')
jlist = alh._get_jlist()
plt.plot(jlist, 'y', label='J')
jblist = alh._get_jblist()
plt.plot(jblist, 'g', label='J-best')
import SwarmPackagePy
from SwarmPackagePy import testFunctions as tf
from SwarmPackagePy import animation, animation3D
import matplotlib.pyplot as plt
#alh = SwarmPackagePy.abfoa1_swarm1(100, tf.easom_function, -10, 10, 2, 20, 16, 4, 2, 12, 0.9, 0.25, 400 )
#animation(alh.get_agents(), tf.easom_function, -10, 10)
#animation3D(alh.get_agents(), tf.easom_function, -10, 10)
#alh = SwarmPackagePy.abfoa2_swarm1(100, tf.thriple_gaussian_function, -10, 10, 50, 100, 16, 4, 2, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,400 )
#alh = SwarmPackagePy.abfoa2_swarm1(100, tf.f2_rosenbrock_function, -32, 32, 30, 100, 16, 4, 2, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,400 )
#alh = SwarmPackagePy.abfoa1_swarm1(100, tf.f2_rosenbrock_function, -32, 32, 30, 100, 16, 4, 2, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,400 )
# OKalh = SwarmPackagePy.abfoa1_swarm1(100, tf.f3_ackley_function, -32, 32, 30, 100, 16, 4, 2, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,400 )
alh = SwarmPackagePy.abfoa1_swarm1(100, tf.f3_ackley_function, -32, 32, 30, 100, 8, 8, 8, 12, 0.1, 0.25, 0.01, 0.2, 0.01, 10,400 )
fits = alh._get_jfits()
print('Fit Val')
#print(fits)
plt.plot(fits, 'b', label='J-fit')
#plt.ylabel('J values')
#plt.show()
jcclist = alh._get_jcclist()
#print('jcclist')
#print(jcclist)
plt.plot(jcclist,'r', label='J-cc')
#plt.show()
steps = alh._get_csteps()
#print('steps')
#print(steps)
import SwarmPackagePy from SwarmPackagePy import testFunctions as tf from SwarmPackagePy import animation, animation3D alh = SwarmPackagePy.swarm(50, tf.ackley_function, -5, 5, 3, 30) animation(alh.get_agents(), tf.ackley_function, -5, 5) animation3D(alh.get_agents(), tf.ackley_function, -5, 5)
import SwarmPackagePy as sw
from SwarmPackagePy import testFunctions as tf
from SwarmPackagePy import animation, animation3D
import numpy as np
sw._version_
swarm_strength = np.random.random_integers(25, 55)
print("Swarm strength =", swarm_strength)
firefly = sw.fa(n=swarm_strength,
function=tf.sum_squares_function,
lb=-10,
ub=10,
dimension=2,
psi=2,
iteration=50)
animation(firefly.get_agents(), tf.sum_squares_function, -10, 10)
animation3D(firefly.get_agents(), tf.sum_squares_function, -10, 10)
import SwarmPackagePy
from SwarmPackagePy import testFunctions as tf
from SwarmPackagePy import animation, animation3D
import matplotlib.pyplot as plt
#alh = SwarmPackagePy.abfoa1_swarm1(100, tf.easom_function, -10, 10, 2, 20, 16, 4, 2, 12, 0.9, 0.25, 400 )
#animation(alh.get_agents(), tf.easom_function, -10, 10)
#animation3D(alh.get_agents(), tf.easom_function, -10, 10)
"""
alh = SwarmPackagePy.abfoa1_swarm2(100, tf.gaussian_function, -50, 50, 50, 100, 16, 4, 2, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,400 )
animation(alh.get_agents(), tf.gaussian_function, -50, 50)
animation3D(alh.get_agents(), tf.gaussian_function, -50, 50)
"""
alh = SwarmPackagePy.abfoa2_swarm2(100, tf.thriple_gaussian_function, -10, 10,
50, 100, 16, 4, 2, 12, 0.1, 0.25, 0.1, 0.2,
0.1, 10, 400)
fits = alh._get_jfits()
print('Fit Val')
print(fits)
plt.plot(fits, 'b', label='J-fit')
#plt.ylabel('J values')
#plt.show()
jcclist = alh._get_jcclist()
print('jcclist')
print(jcclist)
plt.plot(jcclist, 'r', label='J-cc')
#plt.show()
Yd_train = distance_to_erbs(Y_train, erbs)
X_test = testmatrix[:,2:8]
Y_test = testmatrix[:,0:2]
Yd_test = distance_to_erbs(Y_test, erbs)
tup = X_test.shape
inter_step = numpy.zeros(X_test.shape[1])
results = numpy.zeros(Y_test.shape)
nbrs = NearestNeighbors(n_neighbors=k, algorithm='kd_tree', leaf_size=60).fit(X_train)
for i in range(0, tup[0]):
distances, indices = nbrs.kneighbors(X_test[i,:].reshape(1,6))
inter_step = kNN_estimator(indices, distances, Y_train, erbs)
# print(inter_step)
# results[i,:] = SwarmPackagePy.aba(200, fitness, [min_lat, min_long], [max_lat, max_long], 2, 50).get_Gbest()
results[i,:] = SwarmPackagePy.pso(100, fitness, [min_lat, min_long], [max_lat, max_long], 2, 50, 0.1, 1, 1).get_Gbest()
err = distance_to_reference(Y_test, results)
# for i in range(0, err.size):
# err[i] = vincenty((Y_test[i,0], Y_test[i,1]), (results[i,0], results[i,1])).meters
k_measures = [sqrt((err**2).mean()), err.std(), err.max(), err.min()]
print(err)
print(k_measures)
f = open('fichier-test.csv', 'w+')
f.write("lat,lon\n")
for i in range(0,results.shape[0]):
f.write("%.6f, %.6f\n" % (results[i,0], results[i,1]))
# print(results)
# d = distance_to_reference(results, Y_test)
import SwarmPackagePy
from SwarmPackagePy import testFunctions as tf
from SwarmPackagePy import animation, animation3D
alh = SwarmPackagePy.classic_bfo(100, tf.easom_function, -10, 10, 2, 20, 16, 4,
2, 12, 0.1, 0.25, 1 / 16)
animation(alh.get_agents(), tf.easom_function, -10, 10)
animation3D(alh.get_agents(), tf.easom_function, -10, 10)
#SwarmPackagePy.bfo(n, function, lb, ub, dimension, iteration, Nc, Ns, C, Ped)
#FINAL DEMO alh = SwarmPackagePy.z_bfoa_multiniche(100, f, -r, r, 30, 100, 8, 8, 12, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'adaptive1', 'swarm1', 'true')
#4 alh = SwarmPackagePy.z_bfoa(100, f, -r, r, 30, 100, 8, 8, 12, 12, 0.9, 0.25, 0.1, 0.2, 0.1, 10, 400, 0.03, 'adaptive1', 'swarm1', 'true')
#HARSHA BEST2 : Without Improvment to Elimintion-Dispersal
#alh = SwarmPackagePy.z_bfoa(100, f, -r, r, 30, 100, 8, 8, 12, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'adaptive1', 'swarm1', 'false')
#BEST
#alh = SwarmPackagePy.z_bfoa(100, f, -r, r, 30, 100, 8, 8, 12, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 400, 0.03, 'adaptive1', 'swarm1', 'false')
#HARSHA BEST1 DEMO1 Org alh = SwarmPackagePy.z_bfoa(100, f, -r, r, 30, 100, 8, 8, 12, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 400, 0.03, 'adaptive1', 'swarm1', 'false')
#GOOD 1 alh = SwarmPackagePy.z_bfoa(50, f, -r, r, 30, 100, 16, 4, 4, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'adaptive1', 'swarm1', 'false')
#NOT GOOD alh = SwarmPackagePy.z_bfoa(50, f, -r, r, 30, 100, 16, 4, 4, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'adaptive1', 'swarm1', 'true')
alh = SwarmPackagePy.z_bfoa_multiniche(200, f, -r, r, 2, 100, 8, 8, 12, 12,
0.1, 0.25, 0.1, 0.2, 0.1, 10, lamda,
0.03, 'none', 'swarm1', 'false')
fits = alh._get_jfits()
plt.plot(fits, 'b', label='J-fit')
jcclist = alh._get_jcclist()
plt.plot(jcclist, 'r', label='J-cc')
jarlist = alh._get_jarlist()
plt.plot(jarlist, 'g', label='J-ar')
jlist = alh._get_jlist()
plt.plot(jlist, 'y', label='J')
jblist = alh._get_jblist()
#alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f5_griewank_function, -10, 10, 30, 100, 16, 4, 2, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,400 )
#VERYGOOD alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f3_ackley_function, -32, 32, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 )
r = 20
lamda = 400
f = tf.gaussian_multimodal_positive
#f = tf.gaussian_multimodal_positive #32 #lamda=50
#f = tf.f3_ackley_function #lamda=100
#f = tf.f2_rosenbrock_function #lamda=400 not enough.
#ACKLEY
#alh = SwarmPackagePy.z_ibfoa_jun_li(100, tf.f2_rosenbrock_function, -r, r, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100,0.01 )
#alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f2_rosenbrock_function, -r, r, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 )
#alh = SwarmPackagePy.z_bfoa_swarm1_dev1(100, tf.f3_ackley_function, -32, 32, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 )
# SO FAR BEST alh = SwarmPackagePy.z_ibfoa_jun_li(100, f, -r, r, 30, 100, 8, 8, 16, 4, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100,0.03)
#HARSHA alh = SwarmPackagePy.z_ibfoa_jun_li(100, f, -r, r, 30, 100, 6, 8, 10, 5, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100,0.03)
alh = SwarmPackagePy.z_ibfoa_jun_li(100, f, -r, r, 2, 100, 6, 8, 10, 5, 0.1,
0.25, 0.1, 0.2, 0.1, 10, 100, 0.03)
#alh = SwarmPackagePy.bfoa_swarm1(100, f, -r, r, 30, 100, 5, 8, 10, 5, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100)
#alh = SwarmPackagePy.z_bfoa_swarm1_dev1(100, f, -r, r, 30, 100, 6, 8, 10, 5, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100)
#alh = SwarmPackagePy.z_ibfoa_jun_li(100, f, -r, r, 30, 100, 6, 8, 10, 5, 0.1, 0.25, 0.1, 0.2, 0.1, 10,lamda,0.03)
fits = alh._get_jfits()
plt.plot(fits, 'b', label='J-fit')
jcclist = alh._get_jcclist()
plt.plot(jcclist, 'r', label='J-cc')
jarlist = alh._get_jarlist()
jlist = alh._get_jlist()
plt.plot(jlist, 'y', label='J')
import SwarmPackagePy
from SwarmPackagePy import testFunctions as tf
from SwarmPackagePy import animation, animation3D
import matplotlib.pyplot as plt
#alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f1_sphere_function, -100, 100, 3, 100, 8, 4, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,10 )
#alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f5_griewank_function, -10, 10, 30, 100, 16, 4, 2, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,400 )
#VERYGOOD alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f3_ackley_function, -32, 32, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 )
r = 32
f = tf.f3_ackley_function #lamda=100
#f = tf.f2_rosenbrock_function #lamda=400 not enough.
#ACKLEY
#alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f2_rosenbrock_function, -r, r, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 )
alh = SwarmPackagePy.z_bfoa_swarm1_dev1(100, tf.f3_ackley_function, -32, 32,
30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1,
0.2, 0.1, 10, 100)
fits = alh._get_jfits()
plt.plot(fits, 'b', label='J-fit')
jcclist = alh._get_jcclist()
plt.plot(jcclist, 'r', label='J-cc')
jarlist = alh._get_jarlist()
jlist = alh._get_jlist()
plt.plot(jlist, 'y', label='J')
jblist = alh._get_jblist()
plt.plot(jblist, 'g', label='J-best')
import SwarmPackagePy from SwarmPackagePy import testFunctions as tf from SwarmPackagePy import animation, animation3D #alh = SwarmPackagePy.pso(50, tf.easom_function, -10, 10, 2, 20,w=0.5, c1=1, c2=1) #alh = SwarmPackagePy.bfo(n, function, lb, ub, dimension, iteration, Nc, Ns, C, Ped) #alh = SwarmPackagePy.bfo_with_swarm(20, tf.easom_function, -30, 30, 2, 10, 2, 12, 0.2, 1.15, 0.1, 0.2, 0.1, 10) alh = SwarmPackagePy.bfo_with_swarm(4, tf.easom_function, -30, 30, 2, 30, 100, 4, 0.1, 0.25, 0.1, 0.2, 0.1, 10) animation(alh.get_agents(), tf.easom_function, -50, 50) animation3D(alh.get_agents(), tf.easom_function, -50, 50) #SwarmPackagePy.bfo(n, function, lb, ub, dimension, iteration, Nc, Ns, C, Ped)
import SwarmPackagePy
from SwarmPackagePy import testFunctions as tf
from SwarmPackagePy import animation, animation3D
#alh = SwarmPackagePy.pso(50, tf.easom_function, -10, 10, 2, 20,w=0.5, c1=1, c2=1)
#alh = SwarmPackagePy.bfo(n, function, lb, ub, dimension, iteration, Nc, Ns, C, Ped)
#alh = SwarmPackagePy.bfo_with_swarm(20, tf.easom_function, -30, 30, 2, 10, 2, 12, 0.2, 1.15, 0.1, 0.2, 0.1, 10)
alh = SwarmPackagePy.bfo_with_env_swarm(50, tf.easom_function, -30, 30, 3, 30,
100, 4, 0.1, 0.25, 0.1, 0.2, 0.1, 10,
0)
animation3D(alh.get_agents(), tf.easom_function, -50, 50)
animation3D(alh.get_agents(), tf.easom_function, -50, 50)
#SwarmPackagePy.bfo(n, function, lb, ub, dimension, iteration, Nc, Ns, C, Ped)
#f = tf.f2_rosenbrock_function #lamda=400 not enough. #ACKLEY #alh = SwarmPackagePy.z_ibfoa_jun_li(100, tf.f2_rosenbrock_function, -r, r, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100,0.01 ) #alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f2_rosenbrock_function, -r, r, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 ) #alh = SwarmPackagePy.z_bfoa_swarm1_dev1(100, tf.f3_ackley_function, -32, 32, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 ) # SO FAR BEST alh = SwarmPackagePy.z_ibfoa_jun_li(100, f, -r, r, 30, 100, 8, 8, 16, 4, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100,0.03) #HARSHA alh = SwarmPackagePy.z_ibfoa_jun_li(100, f, -r, r, 30, 100, 6, 8, 10, 5, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100,0.03) #alh = SwarmPackagePy.z_ibfoa_jun_li(100, f, -r, r, 2, 100, 6, 8, 10, 5, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100,0.03) #alh = SwarmPackagePy.bfoa_swarm1(100, f, -r, r, 30, 100, 5, 8, 10, 5, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100) #alh = SwarmPackagePy.z_bfoa_swarm1_dev1(100, f, -r, r, 30, 100, 6, 8, 10, 5, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100) #alh = SwarmPackagePy.z_ibfoa_jun_li(100, f, -r, r, 30, 100, 6, 8, 10, 5, 0.1, 0.25, 0.1, 0.2, 0.1, 10,lamda,0.03) #BEST alh = SwarmPackagePy.z_bfoa_multipop(100, f, -r, r, 30, 100, 6, 8, 10, 5, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100,seg, 15) #2D alh = SwarmPackagePy.z_bfoa_multipop(100, f, -r, r, 3, 100, 4, 8, 4, 8, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, seg, 15) fits = alh._get_jfits() plt.plot(fits, 'b', label='J-fit') jcclist = alh._get_jcclist() plt.plot(jcclist,'r', label='J-cc') jarlist = alh._get_jarlist() jlist = alh._get_jlist() plt.plot(jlist,'y', label='J') jblist = alh._get_jblist() plt.plot(jblist,'g', label='J-best')
#HARSHA BEST1 DEMO1 Org alh = SwarmPackagePy.z_bfoa(100, f, -r, r, 30, 100, 8, 8, 12, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 400, 0.03, 'adaptive1', 'swarm1', 'false')
#GOOD 1 alh = SwarmPackagePy.z_bfoa(50, f, -r, r, 30, 100, 16, 4, 4, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'adaptive1', 'swarm1', 'false')
#NOT GOOD alh = SwarmPackagePy.z_bfoa(50, f, -r, r, 30, 100, 16, 4, 4, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'adaptive1', 'swarm1', 'true')
#Different Heuristics for the Reproductions, chem, jcc, jdelta, and weighted_jdelta
n = 100 #50
r = 20
lamda = 100
f = tf.gaussian_diff_multimodal_positive
dim = 2
#alh = SwarmPackagePy.z_bfoa_extended(n, f, -r, r, dim, 100, 16, 2, 8, 12, 0.05, 0.25, 0.05, 0.2, 0.05, 10, lamda, 0.03, 'adaptive1', 'swarm2', 'false','std','jcc')
#alh = SwarmPackagePy.z_bfoa_extended(n, f, -r, r, dim, 100, 16, 2, 8, 12, 0.05, 0.25, 0.05, 0.2, 0.05, 10, lamda, 0.03, 'adaptive1', 'swarm3', 'false','std','jdelta')
#alh = SwarmPackagePy.z_bfoa_extended(n, f, -r, r, dim, 100, 16, 2, 8, 12, 0.1, 0.25, 0.05, 0.2, 0.05, 10, lamda, 0.03, 'adaptive1', 'swarm2', 'false','none','chem')
alh = SwarmPackagePy.z_bfoa_extended(n, f, -r, r, dim, 100, 16, 2, 8, 12, 0.1,
0.25, 0.05, 0.2, 0.05, 10, lamda, 0.03,
'adaptive1', 'swarm2', 'false', 'std',
'chem')
#TEST CASES
#################################################
#7- SUS only model is quite working for the F2 as well - Whaaaaat?
"""
n =100 #50
r = 1
lamda = 500 #500 is BEST
argrep = 'susonly'
f = tf.F2_var1
dim =1
alh = SwarmPackagePy.z_bfoa_extended(n, f, -r, r, dim, 100, 16, 2, 8, 12, 0.05, 0.25, 0.05, 0.2, 0.05, 10, lamda, 0.03, 'adaptive1', 'swarm2', 'false','susonly')
"""
from SwarmPackagePy import multiniche_benchmark as mbtf
from SwarmPackagePy import animation, animation3D
import matplotlib.pyplot as plt
#BEST 2 - with Swarm2-WOW - WOOOOOOOOOOWWWW
n = 100 #50
r = 19
lamda = 100
f = tf.gaussian_diff_multimodal_positive
dim = 2
sigma_share = 0.2 #0.6 NOT WORKING
d_min = 0.2 #1
d_max = 3
clust_alpha = 1
alh = SwarmPackagePy.z_bfoa_multiniche_clustering_v2(
n, f, -r, r, dim, 100, 16, 2, 8, 12, 0.1, 0.25, 0.05, 0.2, 0.05, 10, lamda,
0.03, 'adaptive1', 'swarm2', 'false', sigma_share, d_min, d_max,
clust_alpha)
#TEST CASES
######################################################
'''NOT GOOD WITH DIFF 2
n =100 #50
r = 19
lamda = 100
f = tf.gaussian_diff_multimodal_positive
dim =2
sigma_share = 0.2 #0.6 NOT WORKING
alh = SwarmPackagePy.z_bfoa_multiniche_clustering_v1(n, f, -r, r, dim, 100, 16, 2, 8, 12, 0.05, 0.25, 0.05, 0.2, 0.05, 10, lamda, 0.03, 'adaptive1', 'swarm2', 'false', sigma_share)
'''
#"""
#BEST 2 - with Swarm2-WOW - WOOOOOOOOOOWWWW
n = 100 #50
r = 19
lamda = 100
f = tf.gaussian_diff_multimodal4_positive
dim = 2
sigma_share = 0.2 #0.6 NOT WORKING
d_min = 1 #0.2 #1
d_max = 3
clust_alpha = 2
step_size = 0.1
alh = SwarmPackagePy.z_bfoa_multiniche_sharing_v4(
n, f, -r, r, dim, 100, 16, 2, 8, 12, step_size, 0.25, 0.05, 0.2, 0.05, 10,
lamda, 0.03, 'none'
'''adaptive1 also''', 'swarm2', 'false', sigma_share, d_min, d_max,
clust_alpha)
#alh = SwarmPackagePy.z_bfoa_multiniche_sharing_v4_raw_for_debug(n, f, -r, r, dim, 100, 16, 2, 8, 12, step_size, 0.25, 0.05, 0.2, 0.05, 10, lamda, 0.03, 'none' '''adaptive1 also''', 'swarm2', 'false', sigma_share,d_min, d_max, clust_alpha)
#"""
#LAST TESTED
'''rastrigin Working , Change last Visualize, LAST one is the BEST, This is EXTRA, Only Change is n=100 not 500
n =100 #50
r = 5 #lb =-1 ub=5
lamda = 100
#f = tf.f5_griewank_function
f = tf.f4_rastrigin_function_var1
dim =2
list_exec_time = []
list_functions = [(testFunctions.ackley_function, (0.0, 0.0)),
(testFunctions.easom_function, (math.pi, math.pi)),
(testFunctions.sphere_function, (0.0 , 0.0)),
(testFunctions.bohachevsky_function, (0.0 , 0.0)),
(testFunctions.sum_squares_function, (0.0 , 0.0))]
for l in range(len(list_functions)):
# gets the function
function = list_functions[l][0]
#print("Function: " + ", " + str(function))
#print("Run, Mean Error, Execution Time")
avg_error = 0
avg_delta = 0
for i in range(num_runs):
start = datetime.datetime.now()
alh = SwarmPackagePy.fa(num_agents, function, -10, 10, 2, num_iterations)
end = datetime.datetime.now()
delta = end - start
last_list_pos = alh.get_agents()[-1]
# gets the global optima of the function
global_optima = list_functions[l][1]
error = 0
for j in range(num_agents):
last_pos = last_list_pos[j]
ind_error = ((last_pos[0] - global_optima[0]) ** 2 + (last_pos[1] - global_optima[1]) ** 2) ** 0.5
error += ind_error
error /= num_agents
list_error.append(error)
list_exec_time.append(delta.microseconds)
#print("average error for run " + str(i + 1) + ": " + str(error))
#print("exec time for run " + str(i + 1) + ": " + str(delta,microseconds))
#r = 20
#lamda = 400
#f = tf.F1_var1
#f = tf.F2_var1
n = 100 #50
r = 20
lamda = 1
f = tf.F3_test #tf.F2_var1 #tf.F2_var1
dim = 2
sigma_share = 0.2 #0.6 NOT WORKING
alh = SwarmPackagePy.z_bfoa_multiniche_sharing_v2(n, f, -r, r, dim, 100, 16, 2,
8, 12, 0.05, 0.25, 0.05, 0.2,
0.05, 10, lamda, 0.03,
'adaptive1', 'swarm2',
'false', sigma_share)
"""
n =100 #50
r = 20
lamda = 100
f = tf.gaussian_diff_multimodal4_positive
dim =2
sigma_share = 0.2 #0.6 NOT WORKING
alh = SwarmPackagePy.z_bfoa_multiniche_sharing_v2(n, f, -r, r, dim, 100, 16, 2, 8, 12, 0.05, 0.25, 0.05, 0.2, 0.05, 10, lamda, 0.03, 'adaptive1', 'swarm2', 'false', sigma_share)
"""
"""
n =100 #50
r = 20
lamda = 1 #NOT OWRKING
#NICE WOW
#alh = SwarmPackagePy.z_bfoa_general_v1(100, f, lb, ub, 2, 100, 8, 8, 4, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'none', 'swarm2', 'false', search_type='discrete')
#WOW BEST SO FAR - CELLULAR AUTOMATUM
#alh = SwarmPackagePy.z_bfoa_general_v1(100, f, lb, ub, 2, 100, 8, 8, 4, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'none', 'none', 'false', search_type='discrete')
#GOOD BUT NOT VERY MUCH. BEST VALUE VARY
#alh = SwarmPackagePy.z_bfoa_general_v1(100, f, lb, ub, 2, 100, 8, 8, 4, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'none', 'swarm1', 'false', search_type='discrete')
#alh = SwarmPackagePy.z_bfoa_general_v1(100, f, lb, ub, 2, 100, 8, 8, 4, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'none', 'swarm2', 'false', search_type='discrete')
#alh = SwarmPackagePy.z_bfoa_general_v1_max(100, f, lb, ub, 2, 100, 8, 8, 4, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'none', 'swarm1', 'false', 'discrete','max')
#alh = SwarmPackagePy.z_bfoa_general_v1_max(100, f, lb, ub, 2, 100, 8, 8, 4, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'none', 'none', 'false', 'discrete','max')
alh = SwarmPackagePy.z_bfoa_general_v1_max(100, f, lb, ub, 2, 100, 8, 8, 4, 12,
0.1, 0.25, 0.1, 0.2, 0.1, 10, 100,
0.03, 'adaptive1', 'swarm2',
'false', 'continuous', 'min')
#TEST CASES
########################################################################
#DAMN WORSE - Lets change SOme Parametr Nc = 4
'''
n =100 #50
r = 20
lamda = 100
f = tf.gaussian_diff_multimodal_positive
dim =2
alh = SwarmPackagePy.z_bfoa(100, f, -r, r, 2, 100, 8, 8, 12, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10, 100, 0.03, 'none', 'swarm1', 'false')
'''
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 27 10:12:26 2017
@author: MichelMacSDD
"""
import SwarmPackagePy
from SwarmPackagePy import testFunctions as tf
from SwarmPackagePy import animation, animation3D
alh = SwarmPackagePy.pso(50,
tf.easom_function,
-10,
10,
2,
20,
w=0.5,
c1=1,
c2=1)
animation(alh.get_agents(), tf.easom_function, -10, 10)
animation3D(alh.get_agents(), tf.easom_function, -10, 10)
print("1. Three-hump camel function.")
print("2. Booth function.")
print("3. Beale function.")
print("4. Exit")
chooseFunction = int(input("Choose: "))
while chooseFunction != 4:
if chooseFunction == 1:
print("=== Three-hump camel function ===")
alh = SwarmPackagePy.pso(50,
tf.three_hump_camel_function,
-5,
5,
20,
100,
w=0.5,
c1=1,
c2=1)
print(alh.get_Gbest())
animation3D(alh.get_agents(), tf.three_hump_camel_function, -5, 5)
elif chooseFunction == 2:
print("=== Booth function ===")
alh = SwarmPackagePy.pso(50,
tf.booth_function,
-10,
10,
20,
100,
import SwarmPackagePy
from SwarmPackagePy import testFunctions as tf
from SwarmPackagePy import animation, animation3D
alh = SwarmPackagePy.abfo1(100, tf.easom_function, -10, 10, 2, 20, 16, 4, 2,
12, 0.1, 0.25, 400)
animation(alh.get_agents(), tf.easom_function, -10, 10)
animation3D(alh.get_agents(), tf.easom_function, -10, 10)
#SwarmPackagePy.bfo(n, function, lb, ub, dimension, iteration, Nc, Ns, C, Ped)
from SwarmPackagePy import testFunctions as tf from SwarmPackagePy import animation, animation3D import matplotlib.pyplot as plt #z_ibfoa_jun_li #alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f1_sphere_function, -100, 100, 3, 100, 8, 4, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,10 ) #alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f5_griewank_function, -10, 10, 30, 100, 16, 4, 2, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,400 ) #VERYGOOD alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f3_ackley_function, -32, 32, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 ) r = 20 f = tf.gaussian_multimodal3_positive #32 #lamda=50 #f = tf.f3_ackley_function #lamda=100 #f = tf.f2_rosenbrock_function #lamda=400 not enough. #ACKLEY #alh = SwarmPackagePy.bfoa_swarm1_dev1_rep(100, tf.f2_rosenbrock_function, -r, r, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 ) #alh = SwarmPackagePy.z_bfoa_swarm1_dev1(100, tf.f3_ackley_function, -32, 32, 30, 100, 24, 8, 8, 12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,100 ) alh = SwarmPackagePy.z_bfoa_swarm1_dev1(100, f, -r, r, 30, 100, 8, 8, 12, 8, 0.1, 0.25, 0.1, 0.2, 0.1, 10,50) fits = alh._get_jfits() plt.plot(fits, 'b', label='J-fit') jcclist = alh._get_jcclist() plt.plot(jcclist,'r', label='J-cc') jarlist = alh._get_jarlist() jlist = alh._get_jlist() plt.plot(jlist,'y', label='J') jblist = alh._get_jblist() plt.plot(jblist,'g', label='J-best')
