Repair methods

Repair methods is a constraints handling technique that consists in repairing infeasible individuals of the population to make them approaching the feasible region. In this tutorial, we will learn how to solve a constrained problem with this technique.


The repairing method implemented in PaGMO/PyGMO is extremely simple. It is based on a meta-algorithm where to the initial problem is associated an unconstrained problem obtained simply removing the constraints an leaving unvaried the original objective function. The problem is solved for optimality and after a predefined number of generations, infeasible individuals are repaired. The name of this meta-algorithm is CORE (Constrained Optimization by Random Evolution). The repairing method uses a simple gradient descent methods to repri the infeasibility of the individuals. Thus, the meta algorithm takes two algorithms, one to evolve the main population towards optimality, and one for repairing.


The problem considered here is the problem g05 from the Congress on Evolutionary Computation 2006 (CEC2006). This problem has a cubic ojective function with two linear inequality and three non linear equality constraints. To solve this problem the Differential Evolution (DE) is chosen as the main evolutionary technique and a simplex method as the repair algorithm. The simplex method used is included into the GSL library. This means that PyGMO/PaGMO needs to be compiled with the GSL option activated. The population contains 90 individuals.

First import the PyGMO library and choose the populations size and the number of generation for the meta-algorithm.

In [1]: from PyGMO import *
In [2]: pop_size = 90
In [3]: n_gen = 1000
In [4]: n_repair_gen = 100

Then instantiate the algorithms you wish to use. The generation of the first main algorithm must be set to 1 as the number of iterations is driven by the meta-algorithm.

In [5]: algo_1 = = 1, xtol=1e-30, ftol=1e-30)
In [6]: algo_repair = algorithm.gsl_nm2(max_iter = n_repair_gen, step_size = 0.02, tol = 1e-8)

Select the problem and associate a population to this problem.

In [7]: prob = problem.cec2006(5)
In [8]: pop = population(prob,pop_size)

Creates the meta-algorithm with these informations.

In [9]: algo_meta = algorithm.cstrs_core(algorithm = algo_1, repair_algorithm = algo_repair, gen = n_gen, repair_frequency = 10, repair_ratio = 1., f_tol = 1e-15, x_tol = 1e-15)

A repairing frequency of 10 generations is selected, and all the infeasible individuals are repaired, hence the ratio is set to 1.

The evolution is then performed.

In [10]: pop = algo_meta.evolve(pop)

And finally, print the solutions.

In [11]: print(pop.champion.x)
In [12]: print(pop.champion.f)
In [13]: print(pop.champion.c)
Out [1]:
(746.9608574722797, 955.4049402203846, 0.07150528169781202, -0.4189587300214994)
(6.983932576076768e-05, -3.644692628768098e-05, 2.23429769903305e-05, -0.05953598828068862, -1.0404640117193114)

As a comparison, you can print the best known solution for this particular problem:

In [11]: print(prob.best_x)
In [12]: print(prob.best_f)
In [13]: print(prob.best_c)
Out [2]:
((679.9451482970287, 1026.066976000047, 0.11887636909441043, -0.39623348521517826),)
((9.999999997489795e-05, 9.999999997489795e-05, 9.999999997489795e-05, -0.03489014569041138, -1.0651098543095887),)

Note that you might need to multiple run this tutorial to get a feasible solution.

If for any reason you wish to repair by hand any individual of the population, you can proceed as follow:

In [11]:,algo_repair)

In this case, the individual at index 0 is repaired with the algorithm algo_repair.