Special issue on new trends and challenges of bio-inspired computational intelligence algorithms in massively complex systems

Abstract

Massively complex systems, such as social networks (Camacho, Panizo-LLedot, Bello-Orgaz, Gonzalez-Pardo, & Cambria, 2020; Lara-Cabrera et al., 2017), renewable energy problems (Twidell & Weir, 2015), or Internet-of-Things problems (Lin et al., 2017), generate massive amounts of data. These massively complex systems have attracted the attention of both industrial and research communities, because the analysis of data can generate valuable knowledge about the specific domain. But at the same time, the amount of data generated and the complexity of the problems mean that classical algorithms and approaches do not provide suitable solutions. In this case, it is quite common for computational intelligence (CI) techniques to extract the knowledge. CI can be defined as a set of bio-inspired research areas focused on the study of adaptive mechanisms to enable, or facilitate, intelligent behaviour in complex and changing environments. There are several research fields that compose CI, including swarm intelligence (Gonzalez-Pardo, Jung, & Camacho, 2017), and evolutionary computation (Salcedo-Sanz, Ortiz-Garcýa, Ángel M. Pérez-Bellido, Portilla-Figueras, & Prieto, 2011). This special issue is focused on the application of bio-inspired algorithms to massively complex systems, ranging from concepts and theoretical developments to advances technologies and innovative applications.

Antonio Gonzalez-Pardo

Associate Professor

Lecturer at the Computer Science Department. Main research interests are related to Computational Intelligence and Metaheuristics applied to Social Networks Analysis, and the optimization of graph-based problems.