Der Artikel bietet einen Überblick und eine kommentierte Bibliographie über die Forschung
in multikriterieller kombinatorischer Optimierung (MOCO, multiple objective combinatorial …

Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios.
As evolutionary algorithms possess several characteristics that are desirable for this type of …

This study introduces the Borg multi-objective evolutionary algorithm (MOEA) for many-
objective, multimodal optimization. The Borg MOEA combines-dominance, a measure of …

Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Despite the considerable …

This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective
evolutionary algorithms (MOEAs) and highlights key advances that the water resources field …

Over the past few years, the research on evolutionary algorithms has demonstrated their
niche in solving multiobjective optimization problems, where the goal is to find a number of …

Over the last decade, a variety of evolutionary algorithms (EAs) have been proposed for
solving multiobjective optimization problems. Especially more recent multiobjective …

Randomized search heuristics such as evolutionary algorithms, genetic algorithms,
evolution strategies, ant colony and particle swarm optimization turn out to be highly …

Feature selection is an important and active issue in clustering and classification problems.
By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus …

Abstract In extending the Particle Swarm Optimisation methodology to multi-objective
problems it is unclear how global guides for particles should be selected. Previous work has …