update_points function

Adaptive normalization of population members

Functions to scalarize the members of the population to locate them in a normalized hyperplane, finding the ideal point, nadir point, worst point and the extreme points.

UpdateIdealPoint(object, nObj)
  UpdateWorstPoint(object, nObj)
  PerformScalarizing(population, fitness, smin, extreme_points, ideal_point)
  get_nadir_point(object)

Arguments

• object: An object of class "nsga3".
• nObj: numbers of objective values of the function to evaluate.
• population: individuals of the population until last front.
• fitness: objective values of the population until last front.
• smin: Achievement Escalation Function Index.
• extreme_points: Extreme points of the previous generation to upgrade.
• ideal_point: Ideal point of the current generation to translate objectives.

Returns

Return scalarized objective values in a normalized hyperplane.

References

J. Blank and K. Deb, "Pymoo: Multi-Objective Optimization in Python," in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567.

K. Deb and H. Jain, "An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints," in IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 577-601, Aug. 2014, doi: 10.1109/TEVC.2013.2281535.

Author(s)

Francisco Benitez