Scalarizing functions play a crucial role in multi-objective evolutionary algorithms (MOEAs)
based on decomposition and the R2 indicator, since they guide the population towards …

Simultaneously considering multiple objectives in machine learning has been a popular
approach for several decades, with various benefits for multi-task learning, the consideration …

Optimization is a very broad field of research with a wide spectrum of important applications.
Until the 1950s, optimization was understood as a single-objective optimization, ie, as the …

We propose a method for constrained and unconstrained nonlinear multiobjective
optimization problems that is based on an SQP-type approach. The proposed algorithm …

In this work nonlinear non-convex multiobjective bilevel optimization problems are
discussed using an optimistic approach. It is shown that the set of feasible points of the …

Recently, Gonçalves and Prudente proposed an extension of the Hager–Zhang nonlinear
conjugate gradient method for vector optimization (Comput Optim Appl 76: 889–916, 2020) …

This book provides an introduction to vector optimization with variable ordering structures,
ie, to optimization problems with a vector-valued objective function where the elements in …

We present methods for co-designing rigid robots over control and morphology (including
discrete topology) over multiple objectives. Previous work has addressed problems in single …

We present a rigorous and comprehensive survey on extensions to the multicriteria setting of
three well-known scalar optimization algorithms. Multiobjective versions of the steepest …

Multi-objective optimization problems are often solved by a sequence of parametric single-
objective problems, so-called scalarizations. If the set of nondominated points is finite, the …