Package: MaOEA 0.7.1

MaOEA: Many Objective Evolutionary Algorithm

A set of evolutionary algorithms to solve many-objective optimization. Hybridization between the algorithms are also facilitated. Available algorithms are: 'SMS-EMOA' <doi:10.1016/j.ejor.2006.08.008> 'NSGA-III' <doi:10.1109/TEVC.2013.2281535> 'MO-CMA-ES' <doi:10.1145/1830483.1830573> The following many-objective benchmark problems are also provided: 'DTLZ1'-'DTLZ4' from Deb, et al. (2001) <doi:10.1007/1-84628-137-7_6> and 'WFG4'-'WFG9' from Huband, et al. (2005) <doi:10.1109/TEVC.2005.861417>.

Authors:Dani Irawan [aut, cre]

MaOEA_0.7.1.tar.gz
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MaOEA.pdf |MaOEA.html✨
MaOEA/json (API)

# Install 'MaOEA' in R:
install.packages('MaOEA', repos = c('https://dots26.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/dots26/maoea/issues

On CRAN:

3.78 score 6 stars 195 downloads 2 mentions 36 exports 31 dependencies

Last updated 2 years agofrom:ad62161655. Checks:OK: 1 ERROR: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-winERRORNov 10 2024
R-4.5-linuxERRORNov 10 2024
R-4.4-winERRORNov 10 2024
R-4.4-macERRORNov 10 2024
R-4.3-winERRORNov 10 2024
R-4.3-macERRORNov 10 2024

Exports:AdaptiveNormalizationcmaes_gencompute_R2HVcompute_R2HVCcompute_R2mtchcreateWeightscreateWeightsSobolDTLZ1DTLZ2DTLZ3DTLZ4EvaluateIndividualEvaluatePopulationGetHVContributionGetHypervolumeGetIGDGetLeastContributionGetLeastContributorInitializePopulationLHSinstall_python_dependenciesload_python_dependenciesMOCMAESNormalizeNSGA3optimMaOEASMOCMAESSMSEMOAWFG1WFG2WFG3WFG4WFG5WFG6WFG7WFG8WFG9

Dependencies:classclie1071gluegtoolsherejsonlitelatticelhslifecyclemagrittrMASSMatrixmconnetnsga2RpngpracmaproxyrandtoolboxrappdirsRcppRcppTOMLreticulaterlangrngWELLrprojrootstringistringrvctrswithr

Readme and manuals

Help Manual

Help pageTopics
Many-Objective Evolutionary AlgorithmMaOEA-package MaOEA
Objective space normalization.AdaptiveNormalization
Generator for cmaes_gen class.cmaes_gen
Modified powered tchebyscheff R2-indicator designed to approximate HVcompute_R2HV
Modified tchebyscheff R2-indicator contribution designed to approximate HVcompute_R2HVC
Modified tchebyscheff R2-indicatorcompute_R2mtch
Das and Dennis's structured weight generation, normal boundary intersection (NBI).createWeights
Sobol sequence weightscreateWeightsSobol
The DTLZ1 test function.DTLZ1
The DTLZ2 test function.DTLZ2
The DTLZ3 test function.DTLZ3
The DTLZ4 test function.DTLZ4
Evaluate objective values of a single individualEvaluateIndividual
Evaluate objective value of a set of individualsEvaluatePopulation
Get HV contribution of all points.GetHVContribution
Compute hypervolumeGetHypervolume
Get IGD valueGetIGD
Get least HV contributionGetLeastContribution
Get least HV contributorGetLeastContributor
Initialize population with Latin Hypercube SamplingInitializePopulationLHS
Install python modules required by MaOEA: numpy and PyGMOinstall_python_dependencies
Install python modules required by MaOEA: numpy and PyGMOload_python_dependencies
Multi-Objective CMA-ESMOCMAES
Objective space normalization.Normalize
Elitist Non-dominated Sorting Genetic Algorithm version IIINSGA3
Elitist Non-dominated Sorting Genetic Algorithm version IIIoptimMaOEA
Steady-state Multi-Objective CMA-ESSMOCMAES
S-Metric Selection EMOASMSEMOA
The WFG1 test function.WFG1
The WFG2 test function.WFG2
The WFG3 test function.WFG3
The WFG4 test function.WFG4
The WFG5 test function.WFG5
The WFG6 test function.WFG6
The WFG7 test function.WFG7
The WFG8 test function.WFG8
The WFG9 test function.WFG9