Community Detection in Networks using Bio-inspired Optimization: Latest Developments, New Results and Perspectives with a Selection of Recent Meta-Heuristics

Detecting groups within a set of interconnected nodes is a widely addressed prob- lem that can model a diversity of applications. Unfortunately, detecting the opti- mal partition of a network is a computationally demanding task, usually conducted by means of optimization methods. Among them, randomized search heuristics have been proven to be efficient approaches. This manuscript is devoted to pro- viding an overview of community detection problems from the perspective of bio-inspired computation. To this end, we first review the recent history of this research area, placing emphasis on milestone studies contributed in the last five years. Next, we present an extensive experimental study to assess the performance of a selection of modern heuristics over weighted directed network instances. Specifically, we combine seven global search heuristics based on two different similarity metrics and eight heterogeneous search operators designed ad-hoc. We compare our methods with six different community detection techniques over a benchmark of 17 Lancichinetti-Fortunato-Radicchi network instances. Ranking statistics of the tested algorithms reveal that the proposed methods perform com- petitively, but the high variability of the rankings leads to the main conclusion: no clear winner can be declared. This finding aligns with community detection tools available in the literature that hinge on a sequential application of different algorithms in search for the best performing counterpart. We end our research by sharing our envisioned status of this area, for which we identify challenges and opportunities which should stimulate research efforts in years to come

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Presence of sst5TMD4, a truncated splice variant of the somatostatin receptor subtype 5, is associated to features of increased aggressiveness in pancreatic neuroendocrine tumors

Purpose: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are rare and heterogeneous tumors, and their biological behavior is not well known. We studied the presence and potential functional roles of somatostatin receptors (sst1-5), focusing particularly on the truncated variants (sst5TMD4, sst5TMD5) and on their relationships with the angiogenic system (Ang/Tie-2 and VEGF) in human GEP-NETs. Experimental Design: We evaluated 42 tumor tissue samples (26 primary/16 metastatic) from 26 patients with GEP-NETs, and 30 non-tumoral tissues (26 from adjacent non-tumor regions and 4 from normal controls) from a single center. Expression of sst1-5, sst5TMD4, sst5TMD5, Ang1-2, Tie-2 and VEGF was analyzed using real-time qPCR, immunofluorescence and immunohistochemistry. Expression levels were associated with tumor characteristics and clinical outcomes. Functional role of sst5TMD4 was analyzed in GEP-NET cell lines. Results: sst1 exhibited the highest expression in GEP-NET, whilst sst2 was the most frequently observed sst-subtype (90.2%). Expression levels of sst1, sst2, sst3, sst5TMD4, and sst5TMD5 were significantly higher in tumor tissues compared to their adjacent non-tumoral tissue. Lymph-node metastases expressed higher levels of sst5TMD4 than in its corresponding primary tumor tissue. sst5TMD4 was also significantly higher in intestinal tumor tissues from patients with residual disease of intestinal origin compared to those with non-residual disease. Functional assays demonstrated that the presence of sst5TMD4 was associated to enhanced malignant features in GEP-NET cells. Angiogenic markers correlated positively with sst5TMD4, which was confirmed by immunohistochemical/fluorescence studies. Conclusions: sst5TMD4 is overexpressed in GEP-NETs and is associated to enhanced aggressiveness, suggesting its potential value as biomarker and target in GEP-NETs.This work has received the following grants: Proyectos de Investigación en Salud (FIS) PI13-01414, and PIE-0041 (funded by Instituto de Salud Carlos III) and S2011/BMD-2328 TIRONET (funded by Comunidad de Madrid) (to MM). BIO-0139, CTS-5051, CTS-1406, PI-0369-2012, BFU2010-19300, BFU2013-43282-R, PI13/00651, CIBERobn and Ayuda Merck Serono 2013 (to RML and JPC). Fellowship CTS-5051 (to AIC). “Sara Borrell” program CD11/00276 (to MDG

A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy

A real-world newspaper distribution problem with recycling policy is tackled in this work. In order to meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries, variable costs and forbidden paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the literature. For this reason, a benchmark composed by 15 instances has been also proposed. In the design of this benchmark, real geographical positions have been used, located in the province of Bizkaia, Spain. For the proper treatment of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed. This application is the first application of the firefly algorithm to any rich vehicle routing problem. To prove that the proposed DFA is a promising technique, its performance has been compared with two other well-known techniques: an evolutionary algorithm and an evolutionary simulated annealing. Our results have shown that the DFA has outperformed these two classic meta-heuristics