Hi there,

I was just wondering how to use the experimenter using ones own problem rather than a predefined problem?

Run the algorithm

N = 2000 problem = Problem(multiple_days_assigned_size, 2, 2) problem.directions[1] = Problem.MINIMIZE problem.directions[0] = Problem.MAXIMIZE problem.constraints[:] = ">=0" problem.types[:] = Integer(0, my_counter_again - 1) problem.function = lambda x: evaluation_function2(x, jobs, c_time_added_up, total_allowed_time_all_jobs, extra_time) algorithm = NSGAII(problem) algorithm.run(N) feasible_solutions_total = [s for s in algorithm.result if s.feasible] Nondominated_solutions_total = nondominated(algorithm.result)

calculate the difference in models nsga2 and nsga3

if name == "main": algorithms = [NSGAII, (NSGAIII, {"divisions_outer":12})] problems = [Problem(multiple_days_assigned_size, 2, 2)] #what would I put in here?

# run the experiment
results = experiment(algorithms, problems, nfe=10000, seeds=10)

Any tips would be greatly appreciated!

Thanks

The given example in README.md for a biobjective problem doesn't work when platypus was installed using pip.

from platypus import NSGAII, Problem, Real This leads to the error ImportError: cannot import name 'NSGAII'

The correct form is:

from platypus.problems import Problem
from platypus.algorithms import NSGAII
from platypus.types import Real

While running ProcessPoolEvaluator in Windows iPython environment, I have the following error.

BrokenProcessPool: A process in the process pool was terminated abruptly while the future was running or pending.

Hi David,

I have the following code:

random.seed(1969)

def inverse_third_layer(x): b= inverse_desired_output
W= weights_third_layer bias= third_layer_bias

return [np.sum(W[0,:])*x[0] - np.sum(b + bias), np.sum(W[1,:])*x[1] - np.sum(b + bias), np.sum(W[2,:])*x[2] - np.sum(b + bias), np.sum(W[3,:])*x[3] - np.sum(b + bias), 
        np.sum(W[4,:])*x[4] - np.sum(b + bias), np.sum(W[5,:])*x[5] - np.sum(b + bias), np.sum(W[6,:])*x[6] - np.sum(b + bias), np.sum(W[7,:])*x[7] - np.sum(b + bias)], [x[0], 
              x[1], x[2], x[3], x[4], x[5], x[6], x[7]]

problem= Problem(8, 8, 8)

problem.types[:]= Real(0, 1) problem.constraints[:]= ">=0" problem.constraints[:]= "<=1" problem.function= inverse_third_layer

algorithm= NSGAII(problem, population_size= 50, variator= UNDX()) algorithm.run(1000)

From it, I get a population of 50 feasible solutions. I would like to compare each objective vector by using the Hypervolume function. For that, I was hoping to iterate through each population to compute the Hypervolume as follows: hyp= Hypervolume(minimum= [0,0,0,0,0,0,0,0], maximum= [2,2,2,2,2,2,2,2]) hyp_result= hyp.calculate(algorithm.result[0])

which gives this error message: TypeError: 'Solution' object is not iterable

Do you have any suggestions?

Many thanks, Ivan

Hi,

I have been using the parallelization aspect of Platypus. However the code never finishes, after hours of leaving it to run. I'm not sure if there is something else I need to download?

problem = Problem(multiple_days_assigned_size, 2, 2)
problem.directions[1] = Problem.MINIMIZE
problem.directions[0] = Problem.MINIMIZE
problem.constraints[:] = ">=0"
problem.types[:] = Integer(0, my_counter_again - 1)
problem.function = lambda x: evaluation_function2(x, jobs, c_time_added_up, total_allowed_time_all_jobs,
                                                  extra_time)

# calculate the difference in models nsga2 and nsga3
if __name__ == "__main__":
    algorithms = [NSGAII, (NSGAIII, {"divisions_outer":12})]
    with ProcessPoolEvaluator(4) as evaluator:
        results = experiment(algorithms, [problem], nfe=2, evaluator=evaluator)

        # Calculate the hypervolume using assigned objective max and mins for comparing algorithms
        min_pri = 0
        min_cost = multiple_days_assigned_size * 30
        max_pri = multiple_days_assigned_size * 6.53
        max_cost = multiple_days_assigned_size * 130

        # calculate the hypervolume indicator between the algorithms
        hyp = Hypervolume(minimum=[min_pri, min_cost], maximum=[max_pri, max_cost])
        hyp_result = calculate(results, hyp, evaluator=evaluator)
        display(hyp_result, ndigits=3)

Hi everyone,

This question is a rather long one. It is referring to this example code: https://github.com/Project-Platypus/Platypus/blob/master/examples/tsp.py

I am working on a multiobjective TSP model where the salesman can choose between bus or flight to go from city i to j. The two conflicting objectives are to minimise travelling time and carbon emissions. This would be applicable to a tourist doing a citytrip, the tourist has the choice whether to go by plane (fast but carbon intensive) or by bus (slow but green).

I created my own "distance matrices" for carbon emissions and traveling time for 6 cities. This dataset I would like to feed the NSGA-II algorithm to find out the non-dominated solutions. Below is just the carbon emission matrix:

carbon_bus = {'Berlin': [999999.0, 4.58, 8.31, 20.71, 35.65, 13.89], 
              'Dresden': [4.58, 999999.0, 3.54, 15.96, 32.68, 10.92], 
              'Prague': [8.31, 3.54, 999999.0, 12.47, 30.88, 9.1], 
              'Budapest': [20.71, 15.96, 12.47, 999999.0, 28.83, 15.48], 
              'Rome': [35.65, 32.68, 30.88, 28.83, 999999.0, 21.73], 
              'Munich': [13.89, 10.92, 9.1, 15.48, 21.73, 999999.0]}

carbon_bus = pd.DataFrame(carbon_bus)
carbon_bus["carbon_bus(kg CO2)"] = ["Berlin","Dresden","Prague","Budapest","Rome","Munich"]
carbon_bus = carbon_bus.set_index("carbon_bus(kg CO2)")
print(carbon_bus)

I tried writing my emissions function based on the tsp()-function from the example code (https://github.com/Project-Platypus/Platypus/blob/master/examples/tsp.py):

def emissions(x):
    em = 0
    tour = x[0]
    for i in range(len(tour)-1):
        em = em + carbon_bus.iloc[tour[i],tour[i+1%len(tour)]]
    return em

I tried to feed this function into the solver but failed to get a reasonable solution:

problem = Problem(1, 1)
problem.types[0] = Permutation(range(len(6))) 
problem.directions[0] = Problem.MINIMIZE
problem.function = emissions
algorithm = GeneticAlgorithm(problem)
algorithm.run(1000, callback = lambda a : print(a.nfe, unique(nondominated(algorithm.result))[0]))

Questions:

1. How can I minimise the carbon objective by referring to the matrix given above?
2. Also, I don't know how to understand the line with tour=x[0] which I have taken from the tsp()-function. Is this referring to the decision variable x_ij?
3. How would you propose to implement a multiobjective TSP in playtypus?

Thanks in advance Kevin

For more information on my model you can refer to my question in OR-Stackexchange: https://or.stackexchange.com/q/3933/3385

Trying to run the following problem with two objectives, three variables, no constraints and Integer variables with SMPSO:

from platypus import *

def my_function(x):
    """ Some objective function"""
    return [-x[0] ** 2 - x[2] ** 2, x[1] - x[0]]

def AsInteger():

    problem = Problem(3, 2)  # define 3 inputs and 1 objective (and no constraints)
    problem.directions[:] = Problem.MAXIMIZE
    int1 = Integer(-50, 50)
    int2 = Integer(-50, 50)
    int3 = Integer(-50, 50)
    problem.types[:] = [int1, int2, int3]
    problem.function = my_function
    algorithm = SMPSO(problem)
    algorithm.run(10000)

I get:

Traceback (most recent call last): File "D:\MyProjects\Drilling\test_platypus.py", line 62, in AsInteger() File "D:\MyProjects\Drilling\test_platypus.py", line 19, in AsInteger algorithm.run(10000) File "build\bdist.win-amd64\egg\platypus\core.py", line 405, in run File "build\bdist.win-amd64\egg\platypus\algorithms.py", line 820, in step File "build\bdist.win-amd64\egg\platypus\algorithms.py", line 838, in iterate File "build\bdist.win-amd64\egg\platypus\algorithms.py", line 1008, in _update_velocities TypeError: unsupported operand type(s) for -: 'list' and 'list'

With OMOPSO:

Traceback (most recent call last): File "D:\MyProjects\Drilling\test_platypus.py", line 62, in AsInteger() File "D:\MyProjects\Drilling\test_platypus.py", line 19, in AsInteger algorithm.run(10000) File "build\bdist.win-amd64\egg\platypus\core.py", line 405, in run File "build\bdist.win-amd64\egg\platypus\algorithms.py", line 941, in step File "build\bdist.win-amd64\egg\platypus\algorithms.py", line 949, in initialize File "build\bdist.win-amd64\egg\platypus\core.py", line 818, in iadd File "build\bdist.win-amd64\egg\platypus\core.py", line 785, in add File "build\bdist.win-amd64\egg\platypus\core.py", line 707, in compare KeyError: 0

With CMAES:

Traceback (most recent call last): File "D:\MyProjects\Drilling\test_platypus.py", line 62, in AsInteger() File "D:\MyProjects\Drilling\test_platypus.py", line 19, in AsInteger algorithm.run(10000) File "build\bdist.win-amd64\egg\platypus\core.py", line 405, in run File "build\bdist.win-amd64\egg\platypus\algorithms.py", line 1074, in step File "build\bdist.win-amd64\egg\platypus\algorithms.py", line 1134, in initialize File "build\bdist.win-amd64\egg\platypus\algorithms.py", line 1298, in iterate File "build\bdist.win-amd64\egg\platypus\core.py", line 378, in evaluate_all File "build\bdist.win-amd64\egg\platypus\evaluator.py", line 88, in evaluate_all File "build\bdist.win-amd64\egg\platypus\evaluator.py", line 55, in run_job File "build\bdist.win-amd64\egg\platypus\core.py", line 345, in run File "build\bdist.win-amd64\egg\platypus\core.py", line 518, in evaluate File "build\bdist.win-amd64\egg\platypus\core.py", line 160, in call File "build\bdist.win-amd64\egg\platypus\types.py", line 147, in decode

File "build\bdist.win-amd64\egg\platypus\tools.py", line 521, in gray2bin TypeError: 'float' object has no attribute 'getitem'

With GDE3:

Traceback (most recent call last): File "D:\MyProjects\Drilling\test_platypus.py", line 62, in AsInteger() File "D:\MyProjects\Drilling\test_platypus.py", line 19, in AsInteger algorithm.run(10000) File "build\bdist.win-amd64\egg\platypus\core.py", line 405, in run File "build\bdist.win-amd64\egg\platypus\algorithms.py", line 67, in step File "build\bdist.win-amd64\egg\platypus\algorithms.py", line 323, in iterate File "build\bdist.win-amd64\egg\platypus\operators.py", line 258, in evolve TypeError: float() argument must be a string or a number

It works fine with NSGAII, for example. Am I missing something fundamental? My apologies if this limitation is mentioned somewhere, I wasn't able to find it anywhere.

Thank you.

Andrea.

Thanks for creating Platypus.

I've started successfully using Platypus for some simple optimizations. In order to communicate to other people which version of Platypus I'm using (particularly in code that I develop), it would be really useful to have a proper release to reference. Failing that, at least a version number within the code itself would help.

Hi!

I am trying to distribute the fitness evaluations using the MPIPool facility. Unfortunately, my code crashes with the following error message:

Traceback (most recent call last):
  File "main_mpi.py", line 125, in invoke_master
    engine.run(ITERATIONS*INDIVIDUALS) 
  File "build/bdist.linux-i686/egg/platypus/core.py", line 304, in run
  File "build/bdist.linux-i686/egg/platypus/algorithms.py", line 173, in step
  File "build/bdist.linux-i686/egg/platypus/algorithms.py", line 183, in initialize
  File "build/bdist.linux-i686/egg/platypus/algorithms.py", line 72, in initialize
  File "build/bdist.linux-i686/egg/platypus/core.py", line 277, in evaluate_all
  File "build/bdist.linux-i686/egg/platypus/evaluator.py", line 88, in evaluate_all
  File "build/bdist.linux-i686/egg/platypus/mpipool.py", line 195, in map
  File "mpi4py/MPI/Comm.pyx", line 1173, in mpi4py.MPI.Comm.recv
  File "mpi4py/MPI/msgpickle.pxi", line 303, in mpi4py.MPI.PyMPI_recv
  File "mpi4py/MPI/msgpickle.pxi", line 269, in mpi4py.MPI.PyMPI_recv_match
  File "mpi4py/MPI/msgpickle.pxi", line 111, in mpi4py.MPI.Pickle.load
  File "mpi4py/MPI/msgpickle.pxi", line 100, in mpi4py.MPI.Pickle.cloads
TypeError: ('__init__() takes exactly 2 arguments (1 given)', <class 'platypus.mpipool.MPIPoolException'>, ())

I am surely missing something. Do you have any suggestions or insights about the cause? I'll try to provide a minimal example of my code, if necessary.

Thank you!

Hi,

So I have a problem setup with multiple types, Real and Subset. I was wondering if I can use the Multiparent Variator for the Reals ? I tried the line below :

operators = CompoundOperator(SSX(probability=0.1), SPX(nparents=4,noffspring=2), Replace(probability=0.01), PM(probability=0.01))

And got an error like unexpected number of offspring, expected 2, received 3.

Is it even possible to combine these operators or I would have to stick to SBX() for Reals?

Thanks!

Hello, The following code doesn't work as expected :

from platypus import (OMOPSO,
                      Problem,
                      Real,
                      InjectedPopulation)


def schaffer(x):
    return [x[0]**3, (x[0]-2)**3]


def callback_function(algorithm):
    print(algorithm.nfe)


problem = Problem(1, 2)
problem.types[:] = Real(-10, 10)
problem.function = schaffer

algorithm = OMOPSO(problem,
                   swarm_size=20,
                   epsilons=[0.05, 0.05])
algorithm.run(5000,
              callback = callback_function)

init_pop = algorithm.result


algorithm2 = OMOPSO(problem,
                    generator=InjectedPopulation(init_pop),
                    swarm_size=20,
                    epsilons=[0.05, 0.05])

algorithm2.run(5000,
               callback = callback_function)

I expected a result like :

20
40
60
....
5000

20
40
...
5000

but instead i got :

20
40
60
....
5000

19
39
59
...
5019

But it works as expected when i use the usual shaffer function :

def schaffer(x):
    return [x[0]**2, (x[0]-2)**2]

My problem is that my cost function takes a long time to compute and i can't afford to make more functions evaluations than expected.

Bumps actions/stale from 6 to 7.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Hi, I just wanted to know how to select the best results from the Playtpus code that I adapted from LINGO software. Because the Playtpus gives me a lot of good results. For example, I wanted ONLY ONE result, and the best result. How I can do that from this code?

#variaveis

S_preco = 1069.23
F_preco = 1071.09
OB_preco = 2006.66
OR_preco = 2669.21
B_preco = 2540.47
S_custo = 533.17
F_custo = 569.89
OB_custo = 1384.39
OR_custo = 1466.34
B_custo = 2389.89

S_total = 2329278

S_percentoleo = 0.2057
C_percentoleo = 0.0064

OBF_percentoleo = 0.22

OR_massamolar = 873*0.000001
M_massamolar = 32*0.000001
B_massamolar = 292*0.000001
G_massamolar = 92*0.000001

S_capacidade = 3600000
OR_capacidade = 367200
B_capacidade = 887760*(880/1000)

S_demanda = 80638
F_demanda = 398984
OB_demanda = 164700
OR_demanda = 164700
B_demanda = 77634

from platypus import NSGAII, Problem, Real

def belegundu(vars):
    S_comercio = vars[0]
    F_comercio = vars[1]
    OB_comercio = vars[2]
    OR_comercio = vars[3]
    B_total = vars[4]
    S_insumo = vars[5]
    C_insumo = vars[6]
    OB_total = vars[7]
    OR_total = vars[8]
    OR_biodiesel = vars[9]
    MOL = vars[10]
    M_insumo = vars[11]
    G_comercio = vars[12]
    objs = [1*(S_comercio*S_preco - S_comercio*S_custo + F_comercio*F_preco - F_comercio*F_custo + OB_comercio*OB_preco - OB_comercio*OB_custo + OR_comercio*OR_preco - OR_comercio*OR_custo + B_total*B_preco - B_total*B_custo)]
    constrs = [
        S_total - S_comercio - S_insumo,
        S_insumo - C_insumo - F_comercio - OB_total,
        C_insumo - 0.04*S_insumo,
        OB_total - (F_comercio*OBF_percentoleo)/(1 - OBF_percentoleo),
        OB_total - OB_comercio - OR_total,
        OR_total - OR_comercio - OR_biodiesel,
        OR_biodiesel - MOL*OR_massamolar,
        M_insumo - 3*MOL*M_massamolar,
        B_total - 3*MOL*B_massamolar,
        G_comercio - MOL*G_massamolar,
        S_insumo - S_capacidade,
        OR_total - OR_capacidade,
        B_total - B_capacidade,
        S_comercio - S_demanda,
        F_comercio** - F_demanda,
        OB_comercio - OB_demanda,
        OR_comercio - OR_demanda,
        B_total - B_demanda
    ]
    return objs, constrs

problem = Problem(13, 1, 18)
problem.types[:] = [Real(0, 2329278), Real(0, 2329278), Real(0, 2329278), Real(0, 2329278), Real(0, 2329278), Real(0, 2329278), Real(0, 2329278), Real(0, 2329278), Real(0, 2329278), Real(0, 2329278), Real(0, 2329278), Real(0, 2329278), Real(0, 2329278)]
problem.constraints[0] = "==0"
problem.constraints[1] = "==0"
problem.constraints[2] = "==0"
problem.constraints[3] = "==0"
problem.constraints[4] = "==0"
problem.constraints[5] = "==0"
problem.constraints[6] = "==0"
problem.constraints[7] = "==0"
problem.constraints[8] = "==0"
problem.constraints[9] = "==0"
problem.constraints[10] = "<=0"
problem.constraints[11] = "<=0"
problem.constraints[12] = "<=0"
problem.constraints[13] = ">=0"
problem.constraints[14] = ">=0"
problem.constraints[15] = ">=0"
problem.constraints[16] = ">=0"
problem.constraints[17] = ">=0"
problem.function = belegundu
problem.directions[:] = Problem.MAXIMIZE

algorithm = NSGAII(problem)
algorithm.run(1000)

feasible_solutions = [s for s in algorithm.result if s.feasible]

for solution in algorithm.result:
    print(solution.objectives)

for solution in algorithm.result:
    print(solution.variables)

Remember that I adapted from this LINGO code:

MAX = L_soja + L_farelo + L_oleobruto + L_oleorefinado + L_biodiesel;

L_soja = S_comercio*S_preco - S_comercio*S_custo;
L_farelo = F_comercio*F_preco - F_comercio*F_custo;
L_oleobruto = OB_comercio*OB_preco - OB_comercio*OB_custo;
L_oleorefinado = OR_comercio*OR_preco - OR_comercio*OR_custo;
L_biodiesel = B_total*B_preco - B_total*B_custo;

!variaveis de preço e de custo (em R$/t);

S_preco = 1069.23;
F_preco = 1071.09;
OB_preco = 2006.66;
OR_preco = 2669.21;
B_preco = 2540.47;
S_custo = 533.17;
F_custo = 569.89;
OB_custo = 1384.39;
OR_custo = 1466.34;
B_custo = 2389.89;

!quantidade inicial de soja;

S_total = 2329278; !t;

!produção dos subprodutos;

S_total = S_comercio + S_insumo;
S_insumo = C_insumo + F_comercio + OB_total; !t;
C_insumo = 0.04*S_insumo;

S_percentoleo = 0.2057;
C_percentoleo = 0.0064;

OBF_percentoleo = 0.22;
OB_total = (F_comercio*OBF_percentoleo)/(1 - OBF_percentoleo);

!balanceamento das massas do óleo;

OB_total = OB_comercio + OR_total;

!balanceamento das massas do óleo refinado;

OR_total = OR_comercio + OR_biodiesel;

!estequiometria da reação do biodiesel (massas molares em kg/mol);

OR_biodiesel = MOL*OR_massamolar;
M_insumo = 3*MOL*M_massamolar;
B_total = 3*MOL*B_massamolar;
G_comercio = MOL*G_massamolar;

SUM_REAGENTES = OR_biodiesel + M_insumo;
SUM_PRODUTOS = B_total + G_comercio;

!constantes da reação estequiometrica (convertido de g/mol para t/mol) ;

OR_massamolar = 873*0.000001;
M_massamolar = 32*0.000001;
B_massamolar = 292*0.000001;
G_massamolar = 92*0.000001;

!restrições (capacidade maxima);

S_insumo <= S_capacidade;
OR_total <= OR_capacidade;
B_total <= B_capacidade;

!restrições (demanda mínima);

S_comercio >= S_demanda;
F_comercio >= F_demanda;
OB_comercio >= OB_demanda;
OR_comercio >= OR_demanda;
B_total >= B_demanda;

!constantes das restrições (em t);

S_capacidade = 3600000;
OR_capacidade = 367200;
B_capacidade = 887760*(880/1000); !Convertido de metros cubicos (m³) para kg, e consequentemente em toneladas;

S_demanda = 80638;
F_demanda = 398984;
OB_demanda = 164700;
OR_demanda = 164700;
B_demanda = 77634;