def moea(name, solsize, popsize, wscalar_, moea_type, max_gen=float('inf'), timeLimit=float('inf')):
from platypus import HUX, BitFlip, TournamentSelector
from platypus import Problem, Binary
from platypus import NSGAII, NSGAIII, SPEA2
from platyplus.operators import varOr
from platyplus.algorithms import SMSEMOA
time_start = time.perf_counter()
logger.info('Running '+moea_type+' in '+name)
prMutation = 0.1
prVariation = 1-prMutation
vartor = varOr(HUX(), BitFlip(1), prVariation, prMutation)
def evalKnapsack(x):
return wscalar_.fobj([xi[0] for xi in x])
problem = Problem(wscalar_.N, wscalar_.M)
problem.types[:] = [Binary(1) for i in range(wscalar_.N)]
problem.function = evalKnapsack
if moea_type in ['NSGAII', 'NSGAII-2', 'NSGAII-4']:
alg = NSGAII(problem, population_size=popsize,
selector=TournamentSelector(1),
variator=vartor)
elif moea_type in ['NSGAIII', 'NSGAIII-2', 'NSGAIII-4']:
alg = NSGAIII(problem, divisions_outer=3,
population_size=popsize,
selector=TournamentSelector(1),
variator=vartor)
elif moea_type in ['SPEA2', 'SPEA2-2', 'SPEA2-4']:
alg = SPEA2(problem, population_size=popsize,
selector=TournamentSelector(1),
variator=vartor)
elif moea_type in ['SMSdom']:
alg = SMSEMOA(problem, population_size=popsize,
selector=TournamentSelector(1),
variator=vartor,
selection_method = 'nbr_dom')
elif moea_type in ['SMShv']:
alg = SMSEMOA(problem, population_size=popsize,
selector=TournamentSelector(1),
variator=vartor,
selection_method = 'hv_contr')
gen = 1
while gen<max_gen and time.perf_counter()-time_start<timeLimit:
alg.step()
gen+=1
alg.population_size = solsize
alg.step()
moeaSols = [evalKnapsack(s.variables) for s in alg.result]
moea_time = time.perf_counter() - time_start
logger.info(moea_type+' in '+name+' finnished.')
return moeaSols, moea_time
problem = EMSGMOP_problem(n, m, s_i, SA_i, p_i, d_hat_j_tmp, t_hat_ij_tmp,
d_ij, e_ij, theat_ij, fr_ij)
# 默认求最小目标值
problem.directions[:] = Problem.MINIMIZE
# algorithm = EpsMOEA(problem, epsilons=0.5)
# algorithm = MOEAD(problem)
# algorithm = SMPSO(problem)
# algorithm = OMOPSO(problem, epsilons=0.5)
# algorithm = EpsNSGAII(problem, epsilons=0.5)
# algorithm = SPEA2(problem)
# algorithm = GDE3(problem)
algorithm = NSGAII(problem)
# algorithm = NSGAIII(problem,divisions_outer=25)
algorithm.population_size = 10
algorithm.run(Algorithm_Run_NUM)
# feasible_solutions = [s for s in algorithm.result if s.feasible]
# if len(feasible_solutions) > 0:
# print(feasible_solutions.objectives)
# print("------------------")
# print("Population size: ")
# print(len(algorithm.result))
# # The output shows on each line the objectives for a Pareto optimal solution:
# for solution in algorithm.result:
# print(solution.variables)
objectives_result = algorithm.result[0].objectives
x_result = algorithm.result[0].variables
def EMSG(Algorithm_Run_NUM, n, m, s_i, SA_i, p_i, d_hat_j_tmp, t_hat_ij_tmp,
d_ij, e_ij, theat_ij, fr_ij):
ProcessPoolExecutor(max_workers=8)
print("-------EMSGMOP:")
problem = EMSGMOP_problem(n, m, s_i, SA_i, p_i, d_hat_j_tmp, t_hat_ij_tmp,
d_ij, e_ij, theat_ij, fr_ij)
# 默认求最小目标值
# problem.directions[:] = Problem.MINIMIZE
# algorithm = EpsMOEA(problem, epsilons=0.5)
# algorithm = MOEAD(problem)
# algorithm = SMPSO(problem)
# algorithm = OMOPSO(problem, epsilons=0.5)
# algorithm = EpsNSGAII(problem, epsilons=0.5)
# algorithm = SPEA2(problem)
# algorithm = GDE3(problem)
algorithm = NSGAII(problem)
# algorithm = NSGAIII(problem,divisions_outer=25)
algorithm.population_size = 10
algorithm.run(Algorithm_Run_NUM)
# feasible_solutions = [s for s in algorithm.result if s.feasible]
# if len(feasible_solutions) > 0:
# print(feasible_solutions.objectives)
# print("------------------")
# print("Population size: ")
# print(len(algorithm.result))
# # The output shows on each line the objectives for a Pareto optimal solution:
# for solution in algorithm.result:
# print(solution.variables)
objectives_result = algorithm.result[0].objectives
x_result = algorithm.result[0].variables
x_matrix = np.zeros([n, m])
for i in range(n):
for j in range(m):
x_matrix[i][j] = round(x_result[j + i * m])
r_s_i = x_matrix.sum(axis=1)
r_d_j = x_matrix.sum(axis=0)
print("objectives_result:")
print(objectives_result)
# print("x_matrix:")
# print(x_matrix)
print("r_s_i:")
print(r_s_i)
print("std of r_s_i-s_i:")
print(np.std(r_s_i - s_i))
print("r_d_j:")
print(r_d_j)
# plot the results using matplotlib
import matplotlib.pyplot as plt
plt.scatter([s.objectives[0] for s in algorithm.result],
[s.objectives[1] for s in algorithm.result])
plt.xlabel("$f_1(x)$")
plt.ylabel("$f_2(x)$")
plt.show()
return objectives_result, x_matrix
'''
def moea(name,
solsize,
popsize,
wscalar_,
moea_type,
max_gen=float('inf'),
timeLimit=float('inf')):
from platypus import Problem, TournamentSelector
from platypus import NSGAII, NSGAIII, SPEA2
from platyplus.operators import varOr, mutGauss, cxUniform
from platyplus.types import RealGauss
from platyplus.algorithms import SMSEMOA
N = wscalar_.xdim
M = wscalar_.M
time_start = time.perf_counter()
logger.info('Running ' + moea_type + ' in ' + name)
prMutation = 0.1
prVariation = 1 - prMutation
vartor = varOr(cxUniform(), mutGauss(), prVariation, prMutation)
def eval_(theta):
return wscalar_.f(np.array(theta))
problem = Problem(N, M)
problem.types[:] = [RealGauss() for i in range(N)]
problem.function = eval_
if moea_type == 'NSGAII':
alg = NSGAII(problem,
population_size=popsize,
selector=TournamentSelector(1),
variator=vartor)
elif moea_type == 'NSGAIII':
alg = NSGAIII(problem,
divisions_outer=3,
population_size=popsize,
selector=TournamentSelector(1),
variator=vartor)
elif moea_type == 'SPEA2':
alg = SPEA2(problem,
population_size=popsize,
selector=TournamentSelector(1),
variator=vartor)
elif moea_type == 'SMSdom':
alg = SMSEMOA(problem,
population_size=popsize,
selector=TournamentSelector(1),
variator=vartor,
selection_method='nbr_dom')
elif moea_type == 'SMShv':
alg = SMSEMOA(problem,
population_size=popsize,
selector=TournamentSelector(1),
variator=vartor,
selection_method='hv_contr')
gen = 1
while gen < max_gen and time.perf_counter() - time_start < timeLimit:
alg.step()
gen += 1
alg.population_size = solsize
alg.step()
moeaSols = [eval_(s.variables) for s in alg.result]
moea_time = time.perf_counter() - time_start
logger.info(moea_type + ' in ' + name + ' finnished.')
return moeaSols, moea_time
