In this paper we report the benchmarking results of four algorithms on the Strict Box-Constraint Optimization Studies (SBOX-COST) benchmarking suite which, as the name states, employs strict box constraints. These results are compared to the benchmarking results on the Black-box Optimization Benchmarking (BBOB) suite which serves as basis for the SBOX-COST suite, but is less restrivtive with respect to box constraints. SBOX-COST enforces its box constraints by returning an invalid value (∞) whenever a point outside of the bounds is evaluated. We use the following four algorithms from the Nevergrad Toolbox: Estimation of Multivariate Normal Algorithm (EMNA), Differential Evolution (DE), Constrained Optimization BY Linear Approximation (Cobyla) and Particle Swarm Optimization (PSO). All algorithms are employed without parameter tuning. Generally, all algorithms perform quite similiar on both suites but we notice a slight advantage for the algorithms that are allowed to evaluate the functions outside of the bounds. The effect of the advantage varies very much with function ID and in some cases, an algorithm’s performance on a problem from the SBOX-COST suite is even better than on the corresponding problem from the BBOB suite.
|Title of host publication||GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion|
|Number of pages||4|
|Publication status||Published - 15 Jul 2023|