solve_pareto_front_representation

desdeo_mcdm.utilities.solve_pareto_front_representation(problem, step=0.1, eps=1e-06, solver_method='scipy_de')

Pass through to solve_pareto_front_representation_general when the problem for which the front is being calculated for is defined as an MOProblem object.

Computes a representation of a Pareto efficient front from a multiobjective minimizatino problem. Does so by generating an evenly spaced set of reference points (in the objective space), in the space spanned by the supplied ideal and nadir points. The generated reference points are then used to formulate achievement scalaraization problems, which when solved, yield a representation of a Pareto efficient solution.

Parameters

• problem (MOProblem) – The multiobjective minimization problem for which the front is to be solved for.

• step (Optional[Union[np.ndarray, float]], optional) – Either a float or an array of floats. If a single float is given, generates reference points with the objectives having values a step apart between the ideal and nadir points. If an array of floats is given, use the steps defined in the array for each objective’s values. Default to 0.1.

• eps (Optional[float], optional) – An offset to be added to the nadir value to keep the nadir inside the range when generating reference points. Defaults to 1e-6.

• solver_method (Optional[Union[ScalarMethod, str]], optional) – The method used to minimize the achievement scalarization problems arising when calculating Pareto efficient solutions. Defaults to “scipy_de”.

Returns

A tuple containing representations of the Pareto optimal variable values, and the corresponsing objective values.

Return type

Tuple[np.ndarray, np.ndarray]