An Immune Metaheuristics for Large Instances of the Weighted Feedback Vertex Set Problem

Cutello, Vincenzo; Oliva, Maria; Pavone, Mario; Scollo, Rocco A.

doi:10.1109/ssci44817.2019.9002988

Cited by 8 publications

(3 citation statements)

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“…It is important to highlight, also, that all experiments we conducted for the parameters tuning were performed on 30 independent runs, so to have more robust and reliable outcomes. Based on the knowledge acquired on opt-IA in previous research works (Pavone et al 2012;Cutello et al 2020Cutello et al , 2019, the population size was set to d = 100, since it is mainly related to the dimension and complexity of the problem tackled. For all the other parameters, instead, the tuning was determined by evaluating the fitness trend at their different values.…”

Section: Parameters Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In this research work, an immune algorithm, called opt-IA (Cutello et al 2020;Stefano et al 2016;Pavone et al 2012), is presented, which has been designed for detecting communities in complex networks. The proposed opt-IA algorithm is a population-based metaheuristic that takes inspiration from the dynamics and principles of the immune system, and successfully applied in several optimization problems (Cutello et al 2019;Vitale et al 2018;Stracquadanio et al 2015). Three immunological operators are the driving-force of opt-IA -(i) cloning, (ii) hypermutation, and (iii) stochastic aging-which help the algorithm in performing a careful exploration of the search space, avoid getting trapped in local optima, and properly exploiting all information learned during the evolution.…”

Section: Introductionmentioning

confidence: 99%

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Random search immune algorithm for community detection

et al. 2023

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Community detection is a prominent research topic in Complex Network Analysis, and it constitutes an important research field on all those areas where complex networks represent a powerful interpretation tool for describing and understanding systems involved in neuroscience, biology, social science, economy, and many others. A challenging approach to uncover the community structure in complex network, and then revealing the internal organization of nodes, is Modularity optimization. In this research paper, we present an immune optimization algorithm (opt-IA) developed to detect community structures, with the main aim to maximize the modularity produced by the discovered communities. In order to assess the performance of opt-IA, we compared it with an overall of 20 heuristics and metaheuristics, among which one Hyper-Heuristic method, using social and biological complex networks as data set. Unlike these algorithms, opt-IA is entirely based on a fully random search process, which in turn is combined with purely stochastic operators. According to the obtained outcomes, opt-IA shows strictly better performances than almost all heuristics and metaheuristics to which it was compared; whilst it turns out to be comparable with the Hyper-Heuristic method. Overall, it can be claimed that opt-IA, even if driven by a purely random process, proves to be reliable and with efficient performance. Furthermore, to prove the latter claim, a sensitivity analysis of the functionality was conducted, using the classic metrics NMI, ARI and NVI.

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Section: Parameters Tuningmentioning

confidence: 99%