calc_norm_pref_distance

Calculate Normalized Preference Distance
Computes the weighted normalized Euclidean distance between a set of fitness
vectors and a set of reference points.

The 'rmoo' package is a framework for multi- and many-objective
optimization, which allows researchers and users versatility
in parameter configuration, as well as tools for analysis, replication
and visualization of results. The 'rmoo' package was built as a fork of
the 'GA' package by Luca Scrucca(2017) <DOI:10.32614/RJ-2017-008> and
implementing the Non-Dominated Sorting Genetic Algorithms proposed
by K. Deb's.

Francisco Benitez

rmoo

Multi-Objective Optimization in R

Diego P. Pinto-Roa

calc_norm_pref_distance function

<dl><dt>fitness</dt>
<dd>A matrix of fitness values.</dd>
<dt>ref_points</dt>
<dd>A matrix of reference points.</dd>
<dt>weight</dt>
<dd>A numeric vector of weights for each objective.</dd>
<dt>ideal_point</dt>
<dd>A numeric vector of ideal point values.</dd>
<dt>nadir_point</dt>
<dd>A numeric vector of nadir point values.</dd></dl>

Arguments

Calculate Normalized Preference Distance
Computes the weighted normalized Euclidean distance between a set of fitness
vectors and a set of reference points. — calc_norm_pref_distance

<dl>

<dt>fitness</dt>
<dd>A matrix of fitness values.</dd>


<dt>ref_points</dt>
<dd>A matrix of reference points.</dd>


<dt>weight</dt>
<dd>A numeric vector of weights for each objective.</dd>


<dt>ideal_point</dt>
<dd>A numeric vector of ideal point values.</dd>


<dt>nadir_point</dt>
<dd>A numeric vector of nadir point values.</dd>

</dl>

Calculate Normalized Preference Distance
Computes the weighted normalized Euclidean distance between a set of fitness
vectors and a set of reference points.

calc_norm_pref_distance: Calculate Normalized Preference Distance Computes the weighted normalized Euclidean distance between a set of fitness vectors and a set of reference points.

Description

Usage

Value

Arguments