func = F18
epoch = 100
problem_size = 100
from mealpy.swarm_based.WOA import BaseWOA
from mealpy.swarm_based.SpaSA import BaseSpaSA
from mealpy.evolutionary_based.GA import BaseGA
from mealpy.swarm_based.GWO import BaseGWO
from mealpy.human_based.TLO import BaseTLO
from mealpy.human_based.QSA import ImprovedQSA
from mealpy.physics_based.EFO import BaseEFO
temp1 = BaseTLO(func,
problem_size=problem_size,
domain_range=(-100, 100),
log=True,
epoch=epoch,
pop_size=50)
temp1._train__()
temp1 = BaseSpaSA(func,
problem_size=problem_size,
domain_range=(-100, 100),
log=True,
epoch=epoch,
pop_size=50)
temp1._train__()
temp2 = BaseEFO(func,
problem_size=problem_size,
domain_range=(-100, 100),
#-------------------------------------------------------------------------------------------------------%
from opfunu.cec_basic.cec2014_nobias import *
from mealpy.human_based.TLO import BaseTLO, OriginalTLO
## Setting parameters
obj_func = F3
# lb = [-15, -10, -3, -15, -10, -3, -15, -10, -3, -15, -10, -3, -15, -10, -3]
# ub = [15, 10, 3, 15, 10, 3, 15, 10, 3, 15, 10, 3, 15, 10, 3]
lb = [-100]
ub = [100]
problem_size = 2000
batch_size = 25
verbose = True
epoch = 1000
pop_size = 50
md1 = OriginalTLO(obj_func, lb, ub, problem_size, batch_size, verbose, epoch,
pop_size)
best_pos1, best_fit1, list_loss1 = md1.train()
print(md1.solution[0])
print(md1.solution[1])
print(md1.loss_train)
md1 = BaseTLO(obj_func, lb, ub, problem_size, batch_size, verbose, epoch,
pop_size)
best_pos1, best_fit1, list_loss1 = md1.train()
print(md1.solution[0])
print(md1.solution[1])
print(md1.loss_train)
from opfunu.cec_basic.cec2014_nobias import *
from mealpy.human_based.TLO import BaseTLO, OriginalTLO
# Setting parameters
obj_func = F5
verbose = False
epoch = 10
pop_size = 50
# A - Different way to provide lower bound and upper bound. Here are some examples:
## 1. When you have different lower bound and upper bound for each parameters
lb1 = [-3, -5, 1]
ub1 = [5, 10, 100]
md1 = BaseTLO(obj_func, lb1, ub1, verbose, epoch, pop_size)
best_pos1, best_fit1, list_loss1 = md1.train()
print(md1.solution[1])
## 2. When you have same lower bound and upper bound for each parameters, then you can use:
## + int or float: then you need to specify your problem size (number of dimensions)
problemSize = 10
lb2 = -5
ub2 = 10
md2 = BaseTLO(obj_func,
lb2,
ub2,
verbose,
epoch,
pop_size,
problem_size=problemSize) # Remember the keyword "problem_size"
from opfunu.cec_basic.cec2014 import * ## Setting parameters problem_size = 30 func = ObjFunc14(problem_size) domain_range = [-15, 15] log = True epoch = 500 pop_size = 50 # pc = 0.95 # pm = 0.025 # md = BaseGA(func.F1, problem_size, domain_range, log, epoch, pop_size, pc, pm) # best_position, best_fit, list_loss = md._train__() # print(best_fit) md2 = BaseWOA(F18, problem_size, domain_range, log, epoch, pop_size) best_position2, best_fit2, list_loss2 = md2._train__() print(best_fit2) md3 = BaseTLO(F18, problem_size, domain_range, log, epoch, pop_size) best_position3, best_fit3, list_loss3 = md3._train__() print(best_fit3) md4 = BaseHGSO(F18, problem_size, domain_range, log, epoch, pop_size) best_position4, best_fit4, list_loss4 = md4._train__() print(best_fit4) md5 = LevyHGSO(F18, problem_size, domain_range, log, epoch, pop_size) best_position5, best_fit5, list_loss5 = md5._train__() print(best_fit5)
## Setting parameters problem_size = 30 func = ObjFunc14(problem_size) domain_range = [-15, 15] log = True epoch = 1000 pop_size = 50 # pc = 0.95 # pm = 0.025 # md = BaseGA(func.F1, problem_size, domain_range, log, epoch, pop_size, pc, pm) # best_position, best_fit, list_loss = md._train__() # print(best_fit) md2 = BaseWOA(elliptic__, problem_size, domain_range, log, epoch, pop_size) best_position2, best_fit2, list_loss2 = md2._train__() print(best_fit2) md3 = BaseTLO(func.F1, problem_size, domain_range, log, epoch, pop_size) best_position3, best_fit3, list_loss3 = md3._train__() print(best_fit3) md4 = BaseHGSO(func.F5, problem_size, domain_range, log, epoch, pop_size) best_position4, best_fit4, list_loss4 = md4._train__() print(best_fit4) md5 = LevyHGSO(func.F5, problem_size, domain_range, log, epoch, pop_size) best_position5, best_fit5, list_loss5 = md5._train__() print(best_fit5)