The objective that freed me: a multi-objective local search approach for continuous single-objective optimization

Abstract Single-objective continuous optimization can be challenging, especially when dealing with multimodal problems. This work sheds light on the effects that multi-objective may have in single-objective space. For this purpose, we examine inner mechanisms of recently developed sophisticated local search procedure SOMOGSA. method solves problems based first expanding problem an additional objective (e.g., a sphere function) to bi-objective domain and subsequently exploiting structures resulting landscapes. Our study particularly focuses sensitivity multiobjectivization approach w.r.t. (1) parametrization artificial second objective, as well (2) position initial starting points As SOMOGSA is modular framework for encapsulating search, integrate Nelder–Mead optimizer respective module compare performance hybrid its original counterpart. We show significantly boost by multiobjectivization. Hence, combined more metaheuristics, help solve highly future.