Equality constraint handling for surrogate-assisted constrained optimization

Real-world optimization problems are often subject to many constraints. Often, as the volume of the feasible space gets smaller, the problems become more complex. The zero volume of feasible spaces for optimization problems with equality constraints makes them challenging. In this paper, we present an equality constraint handling approach embedded for the first time in a surrogate-assisted optimizer (SACOBRA). The proposed technique starts with an expanded feasible area which gradually shrinks to a volume close to zero. Several well-studied constrained test problems are used as benchmarks and the promising results in terms of efficiency and accuracy are compared with other state-of-the-art algorithms.