Many problems in mathematics, statistics, finance, biology, pharmacology, physics, applied mathematics, economics, and chemistry involve the determination of the global minimum of multidimensional real‐valued functions. Simulated annealing methods have been widely used for different global optimization problems. Multiple versions of simulated annealing have been developed, including classical simulated annealing (CSA), fast simulated annealing (FSA), and generalized simulated annealing (GSA). After revisiting the basic idea of GSA using Tsallis statistics, we implemented a modified GSA approach using the R package GenSA. This package was designed to solve complicated nonlinear objective functions with a large number of local minima. In this chapter, we provide a brief introduction to this R package and demonstrate its utility by solving non‐convexoptimization problems in different fields: physics, environmental science, and finance. We performed a comprehensive comparison between GenSA and other widely used R packages, including rgenoud and DEoptim. GenSA is useful and can provide a solution that is comparable with or even better than that provided by other widely used R packages for optimization.
Part of the book: Computational Optimization in Engineering