The field of parallel metaheuristics is continuously evolving as a result of new technologies
and needs that researchers have been encountering. In the last decade, new models of …

In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …

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 …

Genetic algorithms (GAs) belong to the broader family of generic population based
metaheuristic optimisation algorithms called evolutionary algorithms. These are one of the …

This article provides a short introduction to the evolutionary multiobjective optimization field.
The first part of the article discusses the most representative multiobjective evolutionary …

This paper presents a new multi-objective optimization algorithm in which multi-swarm
cooperative strategy is incorporated into particle swarm optimization algorithm, called multi …

List of References on Evolutionary Multiobjective Optimization Page 1 List of References on
Evolutionary Multiobjective Optimization Carlos A. Coello Coello ccoello@cs.cinvestav.mx …

Over the past decade, subdividing evolutionary search into multiple local evolutionary
searches has been identified as an effective method to search for optimal solutions of multi …

Evolutionary algorithms are among the best multiobjective optimizers. However, they need a
large number of function evaluations. In this paper a meta-model based approach to the …

Evolutionary algorithms generally require a large number of objective function evaluations
which can be costly in practice. These evaluations can be replaced by evaluations of a …