The study deals with the application of evolutionary algorithms for solving a problem of frequency assignment in mobile networks
A collaboration recently established between the Centre for Algorithms, Visualisation, and Evolving Systems (CAVES) belonging to the School of Computing of Edinburgh Napier University, Edinburgh, UK, and the Technological Institute of Renewable Energies (ITER) located at Tenerife, Spain, has allowed a research work regarding the area of problem optimisation to be carried out. This collaboration has achieved its first milestone in the form of a scientific paper which will be presented at the prestigious 2016 IEEE World Congress on Computational Intelligence (WCCI 2016) to be held in July 2016 at Vancouver, Canada.
The work, led by Dr. Eduardo Segredo, involved the application of evolutionary algorithms in order to optimise the assignment of frequencies to a set of transceivers belonging to a mobile network. These types of algorithms provide, at feasible times, high quality solutions, even including the optimal one, for highly complex problems. The development of this work, which consisted of more than 4000 simulations, was supported by the computing resources provided by the supercomputer Teide-HPC.
CAVES encourages researchers from different areas and disciplines to collaborate with the aim of creating new multi-disciplinary synergies and opportunities. Some of its main research lines include data analysis, machine learning techniques, and problem optimisation through heuristic-based and bio-inspired techniques, among others.
The ITER area of Technology, by means of its supercomputing department, collaborates with numerous national and international research groups, like CAVES. Other partners include, but are not limited to, the European Space Agency (ESA) and the Institute of Astrophysics of the Canary Islands.
* "Hybrid Parameter Control Approach Applied to a Diversity-based Multi-objective Memetic Algorithm for Frequency Assignment Problems" to be published in the 2016 IEEE World Congress on Computational Intelligence proceedings.