Good practice proposal for the implementation, presentation, and comparison of metaheuristics for solving routing problems

Researchers who investigate in any area related to computational algorithms (both dening new algorithms or improving existing ones) usually nd large diculties to test their work. Comparisons among dierent researches in this eld are often a hard task, due to the ambiguity or lack of detail in the presentation of the work and its results. On many occasions, the replication of the work conducted by other researchers is required, which leads to a waste of time and a delay in the research advances. The authors of this study propose a procedure to introduce new techniques and their results in the eld of routing problems. In this paper this procedure is detailed, and a set of good practices to follow are deeply described. It is noteworthy that this procedure can be applied to any combinatorial optimization problem. Anyway, the literature of this study is focused on routing problems. This eld has been chosen because of its importance in real world, and its relevance in the actual literature

The ALHAMBRA survey: evolution of galaxy clustering since z ~ 1

We study the clustering of galaxies as function of luminosity and redshift in the range 0.35 < z < 1.25 using data from the Advanced Large Homogeneous Area Medium-Band Redshift Astronomical (ALHAMBRA) survey. The ALHAMBRA data used in this work cover 2.38 deg2 in seven independent fields, after applying a detailed angular selection mask, with accurate photometric redshifts, σz ≲ 0.014(1 + z), down to IAB < 24. Given the depth of the survey, we select samples in B-band luminosity down to Lth ≃ 0.16L* at z = 0.9. We measure the real-space clustering using the projected correlation function, accounting for photometric redshifts uncertainties. We infer the galaxy bias, and study its evolution with luminosity. We study the effect of sample variance, and confirm earlier results that the Cosmic Evolution Survey (COSMOS) and European Large Area ISO Survey North 1 (ELAIS-N1) fields are dominated by the presence of large structures. For the intermediate and bright samples, Lmed ≳ 0.6L*, we obtain a strong dependence of bias on luminosity, in agreement with previous results at similar redshift. We are able to extend this study to fainter luminosities, where we obtain an almost flat relation, similar to that observed at low redshift. Regarding the evolution of bias with redshift, our results suggest that the different galaxy populations studied reside in haloes covering a range in mass between log10[Mh/( h−1 M⊙)] ≳ 11.5 for samples with Lmed ≃ 0.3L* and log10[Mh/( h−1 M⊙)] ≳ 13.0 for samples with Lmed ≃ 2L*, with typical occupation numbers in the range of ∼1–3 galaxies per halo

A Ks-band-selected catalogue of objects in the ALHAMBRA survey

Large scale structure and cosmolog

Application of Artificial Intelligence Techniques to Traffic Prediction and Route Planning, the vision of TIMON project

TIMON is an European research project under the Horizon 2020 programme. The main objective of this project is to provide real-time services through a web based platform and a mobile APP for drivers, Vulnerable Road Users (VRUs) and businesses. These services will contribute to increasing drivers and VRUs assistance and safety. To provide these services, one of the core technologies developed inside TIMON will be the design and development of Artificial Intelligence (AI) techniques for traffic prediction and route planning. The DeustoTech-Mobility research group is in charge of this part of the project. The objective of this technical paper is to describe the approach followed in TIMON to develop traffic congestion prediction and route planning services based on AI techniques and the progress done so far. Additionally, the deployment and the result obtained in the first test done is also detailed in this study