Description
Neighbourhood Functions for Local-Search Algorithms.
Description
Neighbourhood functions are key components of local-search algorithms such as Simulated Annealing or Threshold Accepting. These functions take a solution and return a slightly-modified copy of it, i.e. a neighbour. The package provides a function neighbourfun() that constructs such neighbourhood functions, based on parameters such as admissible ranges for elements in a solution. Supported are numeric and logical solutions. The algorithms were originally created for portfolio-optimisation applications, but can be used for other models as well. Several recipes for neighbour computations are taken from "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658).
README.md
neighbours
Neighbourhood functions are key components of local-search algorithms such as Simulated Annealing or Threshold Accepting. These functions take a solution and return a slightly-modified copy of it, i.e. a neighbour. The package provides a single function, neighbourfun(), that produce a neighbourhood function, based on various parameters such as minimum or maximum values. Currently supported are numeric and logical solutions. The algorithms were originally created for portfolio-optimization models, but can also be used for other models.
Installing the package
The latest released version is available from http://enricoschumann.net. In an R session, just type:
install.packages('neighbours,
repos = c('http://enricoschumann.net/R', getOption('repos')))