We expose and contrast the impact of landscape characteristics on the performance of
search heuristics for black-box multiobjective combinatorial optimization problems. A sound …

The structure of the search space explains the behavior of multiobjective search algorithms,
and helps to design well-performing approaches. In this work, we analyze the properties of …

Abstract Pareto Local Search (PLS) is a simple and effective local search method for tackling
multi-objective combinatorial optimization problems. It is also a crucial component of many …

Exploratory Landscape Analysis (ELA) aims at understanding characteristics of single-
objective continuous (black-box) optimization problems in an automated way. Moreover, the …

The research in evolutionary multi-objective optimization is largely missing a notion of
functional landscapes, which could enable a visual understanding of multimodal multi …

In this article, we attempt to understand and to contrast the impact of problem features on the
performance of randomized search heuristics for black-box multiobjective combinatorial …

We investigate the properties of large-scale multi-objective quadratic assignment problems
(mQAP) and how they impact the performance of multi-objective evolutionary algorithms …

Fitness landscape analysis aims to understand the geometry of a given optimization
problem in order to design more efficient search algorithms. However, there is a very little …

The role of carefully constructed benchmark suites in algorithm design and testing is critical.
Within the continuous multiobjective optimization domain, existing suites include the general …

In this study, we consider a bicriteria multiresource generalized assignment problem. Our
criteria are the total assignment load and maximum assignment load over all agents. We aim …