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moko (version 1.0.3)

Multi-Objective Kriging Optimization

Description

Multi-Objective optimization based on the Kriging metamodel. Important functions: mkm() (builder for the multiobjective models), MVPF() (sequential minimizator using variance reduction), MEGO() (generalization of ParEgo) and HEGO() (minimizator using the expected hypervolume improvement). References are Passos and Luersen (2018) .

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Install

install.packages('moko')

Monthly Downloads

22

Version

1.0.3

License

GPL-3

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Repository

https://github.com/coldfir3/moko

Maintainer

Adriano Passos

Last Published

June 2nd, 2020

Functions in moko (1.0.3)

IGD: Inverted Generational Distance

EHVI: Constrained Expected Hypervolume Improvement

HEGO: Efficient Global Optimization Algorithm based on the Hypervolume criteria

max_EHVI: Maximization of the Expected Hypervolume Improvement criterion

max_EI: Maximization of the Constrained Expected Improvement criterion

A S4 class of multiple Kriging models

MEGO: Multi-Objective Efficient Global Optimization Algorithm based on scalarization of the objectives

Constrained Expected Improvement

MVPF: Minimization of the Variance of the Kriging-Predicted Front

Augmented Tchebycheff function

Test function: The Nowacki Beam

Multi-objective Kriging model

Test functions for optimization

Creates a pareto set from given data

moko: Multi-objective Kriging Optimization

nowacki_beam_tps

True pareto front for the nowacki beam problem

Predicted Pareto front

predict,mkm-method

Predictor for a multiobjective Kriging model

Distance between vector and matrix

Deprecated function