Multi-objective optimization utilities#

pygmo.fast_non_dominated_sorting(points)#

Runs the fast non dominated sorting algorithm on the input points

Parameters

points (2d-array-like object) – the input points

Raises

  • ValueError – if points is malformed

  • TypeError – if points cannot be converted to a vector of vector floats

Returns

(ndf, dl, dc, ndr), where:

  • ndf (list of 1D NumPy int array): the non dominated fronts

  • dl (list of 1D NumPy int array): the domination list

  • dc (1D NumPy int array): the domination count

  • ndr (1D NumPy int array): the non domination ranks

Return type

tuple

Examples

>>> import pygmo as pg
>>> ndf, dl, dc, ndr = pg.fast_non_dominated_sorting(points = [[0,1],[-1,3],[2.3,-0.2],[1.1,-0.12],[1.1, 2.12],[-1.1,-1.1]])
pygmo.nadir(points)#

Computes the nadir point of a set of points, i.e objective vectors. The nadir is that point that has the maximum value of the objective function in the points of the non-dominated front.

Complexity is \(\mathcal{O}(MN^2)\) where \(M\) is the number of objectives and \(N\) is the number of points.

Parameters

points (2d-array-like object) – the input points

Raises

  • ValueError – if points is malformed

  • TypeError – if points cannot be converted to a vector of vector floats

Returns

the nadir point

Return type

1D NumPy float array

pygmo.ideal(points)#

Computes the ideal point of a set of points, i.e objective vectors. The ideal point is that point that has, in each component, the minimum value of the objective functions of the input points.

Complexity is \(\mathcal{O}(MN)\) where \(M\) is the number of objectives and \(N\) is the number of points.

Parameters

points (2d-array-like object) – the input points

Raises

  • ValueError – if points is malformed

  • TypeError – if points cannot be converted to a vector of vector floats

Returns

the ideal point

Return type

1D NumPy float array

pygmo.pareto_dominance(obj1, obj2)#

Returns True if obj1 Pareto dominates obj2, False otherwise. Minimization is assumed.

Each pair of corresponding elements in obj1 and obj2 is compared: if all elements in obj1 are less or equal to the corresponding element in obj2, but at least one is different, True will be returned. Otherwise, False will be returned.

Parameters

  • obj1 (array-like object) – the first list of objectives

  • obj2 (array-like object) – the second list of objectives

Raises

  • ValueError – if the dimensions of obj1 and obj2 are different

  • TypeError – if obj1 or obj2 cannot be converted to a vector of vector floats

Returns

True if obj1 is dominating obj2, False otherwise.

Return type

bool

Examples

>>> import pygmo as pg
>>> pg.pareto_dominance(obj1 = [1,2], obj2 = [2,2])
True
pygmo.non_dominated_front_2d(points)#

Finds the non dominated front of a set of two dimensional objectives. Complexity is \(\mathcal{O}(N \log N)\) and is thus lower than the complexity of calling fast_non_dominated_sorting()

See: Jensen, Mikkel T. “Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms.” IEEE Transactions on Evolutionary Computation 7.5 (2003): 503-515.

Parameters

points (2d-array-like object) – the input points

Raises

  • ValueError – if points contain anything else than 2 dimensional objectives

  • TypeError – if points cannot be converted to a vector of vector floats

Returns

the non dominated fronts

Return type

1D NumPy int array

Examples

>>> import pygmo as pg
>>> pg.non_dominated_front_2d(points = [[0,5],[1,4],[2,3],[3,2],[4,1],[2,2]])
array([0, 1, 5, 4], dtype=uint64)
pygmo.crowding_distance(points)#

An implementation of the crowding distance. Complexity is \(O(M N \log N)\) where \(M\) is the number of objectives and \(N\) is the number of individuals. The function assumes points contain a non-dominated front. Failiure to meet this condition will result in undefined behaviour.

See: Deb, Kalyanmoy, et al. “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II.” Parallel problem solving from nature PPSN VI. Springer Berlin Heidelberg, 2000.

Parameters

points (2d-array-like object) – the input points

Raises

  • ValueError – if points does not contain at least two points, or is malformed

  • TypeError – if points cannot be converted to a vector of vector floats

Returns

the crowding distances

Return type

1D NumPy float array

Examples

>>> import pygmo as pg
>>> pg.crowding_distance(points = [[0,5],[1,4],[2,3],[3,2],[4,1]])
array([inf,  1.,  1.,  1., inf])
pygmo.sort_population_mo(points)#

Sorts a multi-objective, unconstrained, population (intended here as a 2D array-like containing objective vectors) with respect to the following strict ordering:

  • \(f_1 \prec f_2\) if the non domination ranks are such that \(i_1 < i_2\). In case \(i_1 = i_2\), then \(f_1 \prec f_2\) if the crowding distances are such that \(d_1 > d_2\).

Complexity is \(\mathcal{O}(M N^2)\) where \(M\) is the size of the objective vector and \(N\) is the number of individuals.

Note

This function will also work for single objective optimization, i.e. with objective vector of size 1, in which case, though, it is more efficient to sort using python built-in sorting methods.

Parameters

points (2d-array-like object) – the input objective vectors

Raises
Returns

the indexes of the sorted objectives vectors.

Return type

1D NumPy int array

Examples

>>> import pygmo as pg
>>> pop = pg.population(prob = pg.dtlz(prob_id = 3, dim=10, fdim=4), size = 20)
>>> pg.sort_population_mo(points = pop.get_f()) 
array([ 4,  7, 14, 15, 16, 18,  9, 13,  5,  3,  6,  2, 12,  0,  1, 19, 17, 8, 10, 11])
pygmo.select_best_N_mo(points, N)#

Returns (unordered) the best N individuals out of a multi-objective, unconstrained population, (intended here as a 2D array-like containing objective vectors). The strict ordering used is the same as that defined in sort_population_mo()

Complexity is \(\mathcal{O}(M N^2)\) where \(M\) is the number of objectives and \(N\) is the number of individuals.

While the complexity is the same as that of sort_population_mo(), this function is to be preferred when possible in that it avoids to compute the crowidng distance for all individuals and only computes it for the last non-dominated front containing individuals included in the best N.

If N is zero, an empty array will be returned.

Parameters

  • points (2d-array-like object) – the input objective vectors

  • N (int) – The size of the returned list of bests.

Raises
Returns

the indexes of the N best objectives vectors.

Return type

1D NumPy int array

Examples

>>> import pygmo as pg
>>> pop = pg.population(prob = pg.dtlz(prob_id = 3, dim=10, fdim=4), size = 20)
>>> pg.select_best_N_mo(points = pop.get_f(), N = 13) 
array([ 2,  3,  4,  5,  6,  7,  9, 12, 13, 14, 15, 16, 18])
pygmo.decompose_objectives(objs, weights, ref_point, method)#

Decomposes a vector of objectives.

A vector of objectives is reduced to one only objective using a decomposition technique.

Three different possibilities for method are here made available:

  • weighted decomposition,

  • Tchebycheff decomposition,

  • boundary interception method (with penalty constraint).

In the case of \(n\) objectives, we indicate with: \(\mathbf f(\mathbf x) = [f_1(\mathbf x), \ldots, f_n(\mathbf x)]\) the vector containing the original multiple objectives, with: \(\boldsymbol \lambda = (\lambda_1, \ldots, \lambda_n)\) an \(n\)-dimensional weight vector and with: \(\mathbf z^* = (z^*_1, \ldots, z^*_n)\) an \(n\)-dimensional reference point. We also ussume \(\lambda_i > 0, \forall i=1..n\) and \(\sum_i \lambda_i = 1\).

The resulting single objective is thus defined as:

  • weighted decomposition: \(f_d(\mathbf x) = \boldsymbol \lambda \cdot \mathbf f\)

  • Tchebycheff decomposition: \(f_d(\mathbf x) = \max_{1 \leq i \leq m} \lambda_i \vert f_i(\mathbf x) - z^*_i \vert\)

  • boundary interception method (with penalty constraint): \(f_d(\mathbf x) = d_1 + \theta d_2\)

where \(d_1 = (\mathbf f - \mathbf z^*) \cdot \hat {\mathbf i}_{\lambda}\) , \(d_2 = \vert (\mathbf f - \mathbf z^*) - d_1 \hat {\mathbf i}_{\lambda})\vert\) , and \(\hat {\mathbf i}_{\lambda} = \frac{\boldsymbol \lambda}{\vert \boldsymbol \lambda \vert}\)

Note that while ref_point is required, it does not impact the calculation for the weighted method as shown above.

Parameters

  • objs (array-like object) – the objective vectors

  • weights (array-like object) – the weights \(\boldsymbol \lambda\)

  • ref_point (array-like object) – the reference point \(\mathbf z^*\) . It is not used if method is "weighted"

  • method (str) – the decomposition method: one of "weighted", "tchebycheff" or "bi"

Raises

  • ValueError – if objs, weight and ref_point have different sizes or if method is not one of "weighted", "tchebycheff" or "bi".

  • TypeError – if weights or ref_point or objs cannot be converted to a vector of floats.

Returns

a one dimensional array containing the decomposed objective.

Return type

1D NumPy float array

Examples

>>> import pygmo as pg
>>> pg.decompose_objectives(objs = [1,2,3], weights = [0.1,0.1,0.8], ref_point=[5,5,5], method = "weighted") 
array([ 2.7])
>>> pg.decompose_objectives(objs = [1,2,3], weights = [0.1,0.1,0.8], ref_point=[0,0,0], method = "weighted") 
array([ 2.7])
>>> pg.decompose_objectives(objs = [1,2,3], weights = [0.1,0.1,0.8], ref_point=[5,5,5], method = "tchebycheff") 
array([ 1.6])
pygmo.decomposition_weights(n_f, n_w, method, seed)#

Generates the requested number of weight vectors to be used to decompose a multi-objective problem. Three methods are available:

  • "grid" generates weights on an uniform grid. This method may only be used when the number of requested weights to be genrated is such that a uniform grid is indeed possible. In two dimensions this is always the case, but in larger dimensions uniform grids are possible only in special cases

  • "random" generates weights randomly distributing them uniformly on the simplex (weights are such that \(\sum_i \lambda_i = 1\))

  • "low discrepancy" generates weights using a low-discrepancy sequence to, eventually, obtain a better coverage of the Pareto front. Halton sequence is used since low dimensionalities are expected in the number of objectives (i.e. less than 20), hence Halton sequence is deemed as appropriate.

Note

All methods are guaranteed to generate weights on the simplex (\(\sum_i \lambda_i = 1\)). All weight generation methods are guaranteed to generate the canonical weights [1,0,0,…], [0,1,0,..], … first.

Parameters

  • n_f (int) – number of the objective vectors

  • n_w (int) – number of the weights \(\boldsymbol \lambda\)

  • method (str) – the weight generation method: one of "grid", "random", or "low discrepancy"

  • seed (int) – seed used by the internal random number generator

Raises

  • OverflowError – if n_f, n_w or seed are negative or greater than an implementation-defined value

  • ValueError – if n_f and n_w are not compatible with the selected weight generation method or if method is not one of "grid", "random" or "low discrepancy"

Returns

the generated weights

Return type

2D NumPy float array

Examples

>>> import pygmo as pg
>>> pg.decomposition_weights(n_f = 2, n_w = 6, method = "low discrepancy", seed = 33) 
array([[ 1.   ,  0.   ],
       [ 0.   ,  1.   ],
       [ 0.25 ,  0.75 ],
       [ 0.75 ,  0.25 ],
       [ 0.125,  0.875],
       [ 0.625,  0.375]])