smoof-package

smoof: Single and Multi-Objective Optimization test functions.

The smoof R package provides generators for huge set of single- and multi-objective test functions, which are frequently used in the literature to benchmark optimization algorithms. Moreover the package provides methods to create arbitrary objective functions in an object-orientated manner, extract their parameters sets and visualize them graphically. package

Some more details

Given a set of criteria $\mathcal{F} = \{f_1, \ldots, f_m\}$ with each $f_i : S \subseteq \mathbf{R}^d \to \mathbf{R} , i = 1, \ldots, m$ being an objective-function, the goal in Global Optimization (GO) is to find the best solution $\mathbf{x}^* \in S$. The set $S$ is termed the set of feasible soluations. In the case of only a single objective function $f$, - which we want to restrict ourself in this brief description - the goal is to minimize the objective, i. e.,

Sometimes we may be interested in maximizing the objective function value, but since $min(f(\mathbf{x})) = -\min(-f(\mathbf{x}))$, we do not have to tackle this separately. To compare the robustness of optimization algorithms and to investigate their behaviour in different contexts, a common approach in the literature is to use artificial benchmarking functions, which are mostly deterministic, easy to evaluate and given by a closed mathematical formula. A recent survey by Jamil and Yang lists 175 single-objective benchmarking functions in total for global optimization [1]. The smoof package offers implementations of a subset of these functions beside some other functions as well as generators for large benchmarking sets like the noiseless BBOB2009 function set [2] or functions based on the multiple peaks model 2 [3].

References

[1] Momin Jamil and Xin-She Yang, A literature survey of benchmark functions for global optimization problems, Int. Journal of Mathematical Modelling and Numerical Optimisation, Vol. 4, No. 2, pp. 150-194 (2013). [2] Hansen, N., Finck, S., Ros, R. and Auger, A. Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions. Technical report RR-6829. INRIA, 2009. [3] Simon Wessing, The Multiple Peaks Model 2, Algorithm Engineering Report TR15-2-001, TU Dortmund University, 2015.