[SOLVED] Multi-objective optimisation using PyGMO

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!

Solution

Are you looking at the correct documentation?

There is no fdim (which anyway does nothing in your example since it is only a local variable and is not used). But there is n_obj:

n_obj: number of objectives. Defaults to 1

So, I think you want something like (corrected thanks to @Distopia):

#(...)
def __init__(self, fdim=2):
    NUM_PARAMS = 4
    super(my_problem, self).__init__(NUM_PARAMS, 0, fdim)
    self.set_bounds(0.01, 100)
#(...)