In recent years, the development of new algorithms for multiobjective optimization has
considerably grown. A large number of performance indicators has been introduced to …

In many optimization problems arising from scientific, engineering and artificial intelligence
applications, objective and constraint functions are available only as the output of a black …

This article presents the definition of the performance that the electric motor should have in
order to satisfy the requirements of the electric vehicle in terms of acceleration time and …

This work presents recent advances within the AlgoRithms for Global Optimization of
coNstrAined grey-box compUTational problems (ARGONAUT) framework, developed for …

We analyze the conditional gradient method, also known as Frank–Wolfe method, for
constrained multiobjective optimization. The constraint set is assumed to be convex and …

Organ transplant network as one of the most complex and challenging networks among
healthcare systems, requires an effective supply chain design. In this research, a novel bi …

In this paper, we propose an adaptation of the classical augmented Lagrangian method for
dealing with multi-objective optimization problems. Specifically, after a brief review of the …

Addressing real-world optimization problems becomes particularly challenging when
analytic objective functions or constraints are unavailable. While numerous studies have …

Hospitals are huge and complex systems. However, for many years, the management was
commonly focused on improving the quality of the medical care, while less attention was …

The optimization of multimodal functions is a challenging task, in particular when derivatives
are not available for use. Recently, in a directional direct search framework, a clever …