update_points: Adaptive normalization of population members

In rmoo: Multi-Objective Optimization in R

Adaptive normalization of population members

Description

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.

Usage

  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.

Value

Return scalarized objective values in a normalized hyperplane.

Author(s)

Francisco Benitez

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.

rmoo documentation built on Sept. 24, 2022, 9:05 a.m.