Karthik Raman Karthik Raman - 5 months ago 40
Python Question

Multi-objective optimisation using PyGMO

I am using the PyGMO package for Python, for multi-objective optimisation. I am unable to fix the dimension of the fitness function in the constructor, and the documentation is not very descriptive either. I am wondering if anyone here has had experience with PyGMO in the past: this could be fairly simple.

I try to construct a minimum example below:

from PyGMO.problem import base
from PyGMO import algorithm, population
import numpy as np
import matplotlib.pyplot as plt


class my_problem(base):
def __init__(self, fdim=2):
NUM_PARAMS = 4
super(my_problem, self).__init__(NUM_PARAMS)
self.set_bounds(0.01, 100)

def _objfun_impl(self, K):
E1 = K[0] + K[2]
E2 = K[1] + K[3]

return (E1, E2, )


if __name__ == '__main__':
prob = my_problem() # Create the problem
print (prob)
algo = algorithm.sms_emoa(gen=100)
pop = population(prob, 50)
pop = algo.evolve(pop)

F = np.array([ind.cur_f for ind in pop]).T
plt.scatter(F[0], F[1])
plt.xlabel("$E_1$")
plt.ylabel("$E_2$")
plt.show()


fdim=2
above is a failed attempt to set the fitness dimension. The code fails with the following error:

ValueError: ..\..\src\problem\base.cpp,584: fitness dimension was changed inside objfun_impl().


I'd be grateful if someone can help figure this out. Thanks!

Answer

I modified their example and this seemed to work for me.

#(...)
def __init__(self, fdim=2):
    NUM_PARAMS = 4
    # We call the base constructor as 'dim' dimensional problem, with 0 integer parts and 2 objectives.
    super(my_problem, self).__init__(NUM_PARAMS,0,fdim)
    self.set_bounds(0.01, 100)
#(...)