In this paper we present a novel method for finding the strong Nash equilibrium. The approach consists on determining a scalar λ∗ and the corresponding strategies d∗(λ∗) fixing specific bounds (min and max) that belong to the Pareto front. Bounds correspond to restrictions imposed by the player over the Pareto front that establish a specific decision area where the strategies can be selected. W...

In this manuscript, we consider smooth multi-objective optimization problems with convex constraints. We propose an extension of a augmented Lagrangian Method from recent literature. The new algorithm is specifically designed to handle sets points and produce good approximations the whole Pareto front, as opposed original one which converges single solution. prove properties global convergence ...

Physics-informed neural networks (PINNs) have emerged as a promising deep learning method, capable of solving forward and inverse problems governed by differential equations. Despite their recent advance, it is widely acknowledged that PINNs are difficult to train often require careful tuning loss weights when data physics functions combined scalarization multi-objective (MO) problem. In this p...

Low weight and high load capacity are remarkable advantages of sandwich panels with corrugated core, which make them more considerable by engineering structure designers. It’s important to consider the limitations such as yielding and buckling as design constraints for optimal design of these panels. In this paper, multi-objective optimization of sandwich panels with corrugated core is carried ...

This paper proposes a multiobjective harmony search (MOHS) algorithm for optimal power flow (OPF) problem. OPF problem is formulated as a nonlinear constrained multiobjective optimization problem where different objectives and different constraints have been considered. Fast elitist non dominated sorting and crowding distance have been used to find and manage the Pareto optimal front. Finally, ...

Recent works in evolutionary multiobjective optimization suggest to shift the focus from solely evaluating optimization success in the objective space to also taking the decision space into account. They indicate that this may be a) necessary to express the users requirements of obtaining distinct solutions (distinct Pareto set parts or subsets) of similar quality (comparable locations on the P...