Generalized Simulated Annealing for Global Optimization: The GenSA Package

Authors

• Yang Xiang, Philip Morris International R&D
• Sylvain Gubian, Philip Morris International R&D
• Brain Suomela, Philip Morris International R&D
• Julia Hoeng, Philip Morris International R&D

Date of this Version

6-2013

Document Type

Article

Citation

The R Journal (June 2013) 5(1); Editor: Hadley Wickham

Comments

Copyright 2013, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

Many problems in statistics, finance, biology, pharmacology, physics, mathematics, economics, and chemistry involve determination of the global minimum of multidimensional functions. R packages for different stochastic methods such as genetic algorithms and differential evolution have been developed and successfully used in the R community. Based on Tsallis statistics, the R package GenSA was developed for generalized simulated annealing to process complicated non-linear objective functions with a large number of local minima. In this paper we provide a brief introduction to the R package and demonstrate its utility by solving a non-convex portfolio optimization problem in finance and the Thomson problem in physics. GenSA is useful and can serve as a complementary tool to, rather than a replacement for, other widely used R packages for optimization.