Package: MOEADr 1.1.3

MOEADr: Component-Wise MOEA/D Implementation

Modular implementation of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) [Zhang and Li (2007), <doi:10.1109/TEVC.2007.892759>] for quick assembling and testing of new algorithmic components, as well as easy replication of published MOEA/D proposals. The full framework is documented in a paper published in the Journal of Statistical Software [<doi:10.18637/jss.v092.i06>].

Authors:Felipe Campelo [aut, cre], Lucas Batista [com], Claus Aranha [aut]

MOEADr_1.1.3.tar.gz
MOEADr_1.1.3.zip(r-4.5)MOEADr_1.1.3.zip(r-4.4)MOEADr_1.1.3.zip(r-4.3)
MOEADr_1.1.3.tgz(r-4.4-any)MOEADr_1.1.3.tgz(r-4.3-any)
MOEADr_1.1.3.tar.gz(r-4.5-noble)MOEADr_1.1.3.tar.gz(r-4.4-noble)
MOEADr_1.1.3.tgz(r-4.4-emscripten)MOEADr_1.1.3.tgz(r-4.3-emscripten)
MOEADr.pdf |MOEADr.html
MOEADr/json (API)
NEWS

# Install 'MOEADr' in R:
install.packages('MOEADr', repos = c('https://fcampelo.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/fcampelo/moeadr/issues

On CRAN:

moeadmultiobjective-optimization

6.30 score 20 stars 40 scripts 174 downloads 52 exports 2 dependencies

Last updated 2 years agofrom:0ac9b1962e. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winNOTEOct 31 2024
R-4.5-linuxNOTEOct 31 2024
R-4.4-winNOTEOct 31 2024
R-4.4-macNOTEOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:box_constraintscalcIGDcheck_stop_criteriaconstraint_noneconstraint_penaltyconstraint_vbrcreate_populationdecomposition_mslddecomposition_slddecomposition_uniformdefine_neighborhoodevaluate_populationexample_problemfind_nondominated_pointsgenerate_weightsget_constraint_methodsget_decomposition_methodsget_localsearch_methodsget_scalarization_methodsget_stop_criteriaget_update_methodsget_variation_operatorsls_dvlsls_tpqamake_vectorized_smoofmoeadorder_neighborhoodperform_variationpreset_moeadprint_progressscalarization_awtscalarization_ipbiscalarization_pbiscalarization_wsscalarization_wtscalarize_valuesscale_objectivesstop_maxevalstop_maxiterstop_maxtimeunitary_constraintsupdate_populationupdt_bestupdt_restrictedupdt_standardvariation_binrecvariation_diffmutvariation_localsearchvariation_nonevariation_polymutvariation_sbxvariation_truncate

Dependencies:assertthatFNN

Basic Usage of the MOEADr Package

Rendered fromBasic_Usage.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2023-01-06
Started: 2017-08-18

Defining Problems in the MOEADr Package

Rendered fromproblem-definition.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2020-02-17
Started: 2017-03-06

Fine tuning MOEA/D configurations using MOEADr and irace

Rendered fromComparison_Usage.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2022-11-05
Started: 2017-08-18

Testing New Operators using the MOEADr Package

Rendered fromModification_Usage.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2020-02-17
Started: 2017-08-18

Writing Extensions for the MOEADr Package

Rendered fromwriting-functions-for-moeadr.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2020-02-17
Started: 2016-12-14

Readme and manuals

Help Manual

Help pageTopics
Box constraints routinebox_constraints
Inverted Generational DistancecalcIGD
Stop criteria for MOEA/Dcheck_stop_criteria
NULL constraint handling method for MOEA/Dconstraint_none
"Penalty" constraint handling method for MOEA/Dconstraint_penalty
"Violation-based Ranking" constraint handling method for MOEA/Dconstraint_vbr
Create populationcreate_population
Problem Decomposition using Multi-layered Simplex-lattice Designdecomposition_msld
Problem Decomposition using Simplex-lattice Designdecomposition_sld
Problem Decomposition using Uniform Designdecomposition_uniform
Calculate neighborhood relationsdefine_neighborhood
Evaluate populationevaluate_population
Example problemexample_problem
Find non-dominated pointsfind_nondominated_points
Calculate weight vectorsgenerate_weights
Print available constraint methodsget_constraint_methods
Print available decomposition methodsget_decomposition_methods
Print available local search methodsget_localsearch_methods
Print available scalarization methodsget_scalarization_methods
Print available stop criteriaget_stop_criteria
Print available update methodsget_update_methods
Print available variation operatorsget_variation_operators
Differential vector-based local searchls_dvls
Three-point quadratic approximation local searchls_tpqa
Make vectorized smoof functionmake_vectorized_smoof
MOEA/Dmoead
Order Neighborhood for MOEA/Dorder_neighborhood
Run variation operatorsperform_variation
plot.moeadplot.moead
preset_moeadpreset_moead
Print progress of MOEA/Dprint_progress
print.moeadprint.moead
Adjusted Weighted Tchebycheff Scalarizationscalarization_awt
Inverted Penalty-based Boundary Intersection Scalarizationscalarization_ipbi
Penalty-based Boundary Intersection Scalarizationscalarization_pbi
Weighted Sum Scalarizationscalarization_ws
Weighted Tchebycheff Scalarizationscalarization_wt
Scalarize values for MOEA/Dscalarize_values
Scaling of the objective function valuesscale_objectives
Stop criterion: maximum number of evaluationsstop_maxeval
Stop criterion: maximum number of iterationsstop_maxiter
Stop criterion: maximum runtimestop_maxtime
summary.moeadsummary.moead
Unitary constraints routineunitary_constraints
Update populationupdate_population
Best Neighborhood Replacement Update for MOEA/Dupdt_best
Restricted Neighborhood Replacement Update for MOEA/Dupdt_restricted
Standard Neighborhood Replacement Update for MOEA/Dupdt_standard
Binomial Recombinationvariation_binrec
Differential Mutationvariation_diffmut
Local search Operatorsvariation_localsearch
Identity operatorvariation_none
Polynomial mutationvariation_polymut
Simulated binary crossovervariation_sbx
Truncatevariation_truncate