Python SwarmPackagePy.z_bfoa_general_v1_max 예제들

예제 #1

파일: test_z_bfoa_general_v1_max_without_BF.py 프로젝트: dananjayamahesh/MunichBFOR

#f = revf.gaussian_diff_multimodal4_positive_max

#test_function_shape(f, -r, r,dim)

#dim =2

lb = [0 for i in range(dim)]
ub = [r for i in range(dim)]

# 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, dim, 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, dim, 100, 8, 16, 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, dim, 100, 16, 16, 4,
                                           12, 0.1, 0.25, 0.1, 0.2, 0.1, 10,
                                           100, 0.03, 'none', 'none', 'false',
                                           'discrete', 'max')

print('Maximum Revenue : ', revf.MaxRev)
print('Optimum Allocation : ', revf.OptAlloc)
#print('Max Revenue : ',revf.OptAlloc)

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')

예제 #2

#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')
'''