Debido al creciente interés en el análisis de datos en streaming en entornos Big Data para la toma de decisiones, cada vez es más común la aparición de problemas de optimización dinámica que involucran dos o más objetivos ...

Over the years, many interactive multiobjective optimization methods based on a reference point have been proposed. With a reference point, the decision maker indicates desirable objective function values to iteratively ...

Over the past decade the rapid rise of creating data in all domains of knowledge such as traffic, medicine, social network, industry, etc., has highlighted the need for enhancing the process of analyzing large data volumes, ...

Technologies for Big Data and Data Science are receiving increasing research interest nowadays. This paper introduces the prototyping architecture of a tool aimed to solve Big Data Optimization problems. Our tool combines ...

In the field of complex problem optimization with me-taheuristics, semantics has been used for modeling different aspects, such as: problem characterization, parameters, decision-maker's preferences, or algorithms. However, ...

Big Data Optimization is the term used to refer to optimization problems which have to manage very large amounts of data. In this paper, we focus on the parallelization of metaheuristics with the Apache Spark cluster ...

Modern applications of Big Data are transcending from being scalable solutions of data processing and analysis, to now provide advanced functionalities with the ability to exploit and understand the underpinning knowledge. ...