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)Return scalarized objective values in a normalized hyperplane.
An object of class "nsga3".
numbers of objective values of the function to evaluate.
individuals of the population until last front.
objective values of the population until last front.
Achievement Escalation Function Index.
Extreme points of the previous generation to upgrade.
Ideal point of the current generation to translate objectives.
Francisco Benitez
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.