In almost no other field of computer science, the idea of using bio-inspired search paradigms
has been so useful as in solving multiobjective optimization problems. The idea of using a …

Multiobjective optimization plays an increasingly important role in modern applications,
where several criteria are often of equal importance. The task in multiobjective optimization …

Abstract Multi-Task Learning (MTL) is a well established paradigm for jointly learning
models for multiple correlated tasks. Often the tasks conflict, requiring trade-offs between …

The Hausdorff distance d H is a widely used tool to measure the distance between different
objects in several research fields. Possible reasons for this might be that it is a natural …

The continuous and increasing interest concerning vector optimization perceptible in the
research community, where contributions dealing with the theory of duality abound lately …

In many areas in engineering, economics and science new developments are only possible
by the application of modern optimization methods. The optimization problems arising …

Algorithms that make use of the gradient, ie, the direction of maximum improvement, to
search for the optimum of a single-objective function have been around for decades. They …

The Collaborative Research Centre 614" Self-Optimizing Concepts and Structures in
Mechanical Engineering", funded from 2002 to 2013 by the German Research Foundation …

This article proposes a novel predictor–corrector (PC) method for the numerical treatment of
multi-objective optimization problems (MOPs). The algorithm, Pareto Tracer (PT), is capable …

In this paper, a globally convergent Newton-type proximal gradient method is developed for
composite multi-objective optimization problems where each objective function can be …