Package: momst 0.1.1

Jorge A. Parraga-Alava

momst: Multi-Objective Minimum Spanning Tree via NSGA-II with Local Search

Solves the Multi-Criteria Minimum Spanning Tree (mc-MST) problem on complete weighted graphs by combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with optional Pareto local search operators. Chromosomes are represented as Prufer sequences so that every random individual decodes to a valid spanning tree (Cayley's theorem), avoiding repair operators. Four solver variants are provided: pure NSGA-II ("base"), Path Relinking ("PR"), Pareto Local Search ("PLS"), and Tabu Search ("TS"). The package supports 2 and 3 objective formulations and provides convenience functions to plot Pareto fronts and best-compromise spanning trees. This package is the reference implementation of the method described in Parraga-Alava, Inostroza-Ponta and Dorn (2017) <doi:10.1109/CEC.2017.7969432>.

Authors:Jorge A. Parraga-Alava [aut, cre]

momst_0.1.1.tar.gz
momst_0.1.1.zip(r-4.7-any)momst_0.1.1.zip(r-4.6-any)momst_0.1.1.zip(r-4.5-any)
momst_0.1.1.tgz(r-4.6-any)momst_0.1.1.tgz(r-4.5-any)
momst_0.1.1.tar.gz(r-4.7-any)momst_0.1.1.tar.gz(r-4.6-any)
momst_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
momst/json (API)

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

Bug tracker:https://github.com/jorgeklz/momst/issues

Pkgdown/docs site:https://jorgeklz.github.io

On CRAN:

Conda:

4.60 score 270 downloads 17 exports 0 dependencies

Last updated from:590cce14df. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE117
source / vignettesOK222
linux-releaseNOTE134
macos-releaseNOTE79
macos-oldrelNOTE72
windows-develNOTE78
windows-releaseNOTE77
windows-oldrelNOTE69
wasm-releaseOK86

Exports:apply_local_searchbuild_weight_lookupcompute_objectivesdecode_prufergenerate_instancegenerate_prufer_populationnon_dominated_crowdingpareto_local_searchpath_relinkingplot_best_treeplot_pareto_frontrandom_mutationrun_apprun_momsttabu_searchtournament_selectionuniform_crossover

Dependencies:

Comparing the Four MO-MST Variants
Goal of this vignette | Reference | Shared experimental setup | Run all four variants | Inspecting the Pareto fronts numerically | Visual comparison of the four fronts | One panel per variant | All variants superposed | A unified "ground truth" front | Runtime cost | Picking a best-compromise tree per variant | Three-objective example for every variant | Summary
Rendered frommomst-variants.Rmdusingknitr::rmarkdown

Last update: 2026-06-02
Started: 2026-06-02

Getting Started with momst
Overview | Reference | Installation | A first end-to-end example | Step 1. Generate a random bi-objective instance | Step 2. Run the NSGA-II solver (base variant) | Step 3. Inspect the global Pareto front | Step 4. Plot the Pareto front | Step 5. Decode and plot the best-compromise tree | Working with three objectives | Reproducibility | Where to go next
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-06-02
Started: 2026-06-02