Parallelize 'TSP' functions

Henrik Bengtsson

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The futurize package allows you to easily turn sequential code into parallel code by piping the sequential code to the futurize() function. Easy!

TL;DR

library(futurize)
plan(multisession)
library(TSP)

data("USCA50")
tour <- solve_TSP(USCA50, method = "nn", rep = 10L) |> futurize()

Introduction

The TSP package provides algorithms for solving the traveling salesperson problem (TSP).

Example:

Example adopted from help("solve_RSP", package = "TSP"):

library(futurize)
plan(multisession)
library(TSP)

data("USCA50")
methods <- c(
  "identity", "random", "nearest_insertion", "cheapest_insertion",
  "farthest_insertion", "arbitrary_insertion", "nn", "repetitive_nn", 
  "two_opt", "sa"
)

## calculate tours - each tour in parallel
tours <- lapply(methods, FUN = function(m) {
  solve_TSP(USCA50, rep = 10L, method = m) |> futurize()
})
names(tours) <- methods

This will parallelize the computations, given that we have set up parallel workers, e.g.

plan(multisession)

The built-in multisession backend parallelizes on your local computer and works on all operating systems. There are other parallel backends to choose from, including alternatives to parallelize locally as well as distributed across remote machines, e.g.

plan(future.mirai::mirai_multisession)

and

plan(future.batchtools::batchtools_slurm)

Supported Functions

The following TSP functions are supported by futurize():

• solve_TSP()