Package: moocore 0.3.1.900

Manuel López-Ibáñez

moocore: Core Mathematical Functions for Multi-Objective Optimization

Fast implementations of mathematical operations and performance metrics for multi-objective optimization, including filtering and ranking of dominated vectors according to Pareto optimality, hypervolume metric, C.M. Fonseca, L. Paquete, M. López-Ibáñez (2006) <doi:10.1109/CEC.2006.1688440>, epsilon indicator, inverted generational distance, computation of the empirical attainment function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001) <doi:10.1007/3-540-44719-9_15>, and Vorob'ev threshold, expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) <doi:10.1016/j.ejor.2014.07.032>, among others.

Authors:Manuel López-Ibáñez [aut, cre], Carlos Fonseca [ctb], Luís Paquete [ctb], Andreia P. Guerreiro [ctb], Mickaël Binois [ctb], Michael H. Buselli [cph], Wessel Dankers [cph], NumPy Developers [cph], Jean-Sebastien Roy [cph], Makoto Matsumoto [cph], Takuji Nishimura [cph]

moocore_0.3.1.900.tar.gz
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moocore_0.3.1.900.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
moocore/json (API)

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

Bug tracker:https://github.com/multi-objective/moocore/issues

Pkgdown/docs site:https://multi-objective.github.io

Datasets:
  • CPFs - Conditional Pareto fronts obtained from Gaussian processes simulations.
  • HybridGA - Results of Hybrid GA on Vanzyl and Richmond water networks
  • SPEA2minstoptimeRichmond - Results of SPEA2 when minimising electrical cost and maximising the minimum idle time of pumps on Richmond water network.
  • SPEA2relativeRichmond - Results of SPEA2 with relative time-controlled triggers on Richmond water network.
  • SPEA2relativeVanzyl - Results of SPEA2 with relative time-controlled triggers on Vanzyl's water network.
  • tpls50x20_1_MWT - Various strategies of Two-Phase Local Search applied to the Permutation Flowshop Problem with Makespan and Weighted Tardiness objectives.

On CRAN:

Conda:

cmatlabmulti-objective-optimizationmultiobjectivemultiobjective-optimizationnumerical-optimizationoctavepython

10.19 score 58 stars 30 packages 21 scripts 11k downloads 33 exports 3 dependencies

Last updated from:866f3825eb. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK297
linux-devel-x86_64OK241
source / vignettesOK217
linux-release-arm64OK286
linux-release-x86_64OK220
macos-release-arm64OK108
macos-release-x86_64OK233
macos-oldrel-arm64OK170
macos-oldrel-x86_64OK235
windows-develOK216
windows-releaseOK170
windows-oldrelOK189
wasm-releaseOK166

Exports:any_dominatedas_double_matrixattsurf2dfavg_hausdorff_distchoose_eafdiffcompute_eaf_callcompute_eafdiff_calleafeaf_as_listeafdiffepsilon_additiveepsilon_multfilter_dominatedgenerate_ndsethv_approxhv_contributionshypervolumeigdigd_plusis_nondominatedlargest_eafdiffnormalisepareto_rankr2_exactrbind_datasetsread_datasetstotal_whv_recttransform_maximisevorob_devvorob_twhv_hypewhv_rectwrite_datasets

Dependencies:matrixStatsrbibutilsRdpack

Readme and manuals

Help Manual

Help pageTopics
Convert input to a matrix with '"double"' storage mode ('base::storage.mode()').as_double_matrix
Convert a list of attainment surfaces to a single EAF 'data.frame'.attsurf2df
Interactively choose according to empirical attainment function differenceschoose_eafdiff
Same as 'eaf()' but performs no checks and does not transform the input or the output. This function should be used by other packages that want to avoid redundant checks and transformations.compute_eaf_call
Same as 'eafdiff()' but performs no checks and does not transform the input or the output. This function should be used by other packages that want to avoid redundant checks and transformations.compute_eafdiff_call
Conditional Pareto fronts obtained from Gaussian processes simulations.CPFs
Exact computation of the Empirical Attainment Function (EAF)eaf
Convert an EAF data frame to a list of data frames, where each element of the list is one attainment surface. The function 'attsurf2df()' can be used to convert the list into a single data frame.eaf_as_list
Compute empirical attainment function differenceseafdiff
Epsilon metricepsilon epsilon_additive epsilon_mult
Generate a random set of mutually nondominated pointsgenerate_ndset
Approximate the hypervolume indicator.hv_approx
Hypervolume contribution of a set of pointshv_contributions
Results of Hybrid GA on Vanzyl and Richmond water networksHybridGA
Hypervolume metrichypervolume
Inverted Generational Distance (IGD and IGD+) and Averaged Hausdorff Distanceavg_hausdorff_dist igd IGDX igd_plus
Identify and remove dominated points according to Pareto optimalityany_dominated filter_dominated is_nondominated
Identify largest EAF differenceslargest_eafdiff
Normalise pointsnormalise
Rank points according to Pareto-optimality (nondominated sorting).pareto_rank
Exact R2 indicatorr2_exact
Combine datasets 'x' and 'y' by row taking care of making all sets unique.rbind_datasets
Read several data setsread_datasets
Results of SPEA2 when minimising electrical cost and maximising the minimum idle time of pumps on Richmond water network.SPEA2minstoptimeRichmond
Results of SPEA2 with relative time-controlled triggers on Richmond water network.SPEA2relativeRichmond
Results of SPEA2 with relative time-controlled triggers on Vanzyl's water network.SPEA2relativeVanzyl
Various strategies of Two-Phase Local Search applied to the Permutation Flowshop Problem with Makespan and Weighted Tardiness objectives.tpls50x20_1_MWT
Transform matrix according to maximise parametertransform_maximise
Vorob'ev threshold, expectation and deviationvorob_dev vorob_t
Approximation of the (weighted) hypervolume by Monte-Carlo sampling (2D only)whv_hype
Compute (total) weighted hypervolume given a set of rectanglestotal_whv_rect whv_rect
Write data setswrite_datasets