Agent-Based Simulations, Adaptive Algorithms and Solvers (ABS-AAS) Session 3

Time and Date: 10:15 - 11:55 on 14th June 2019

Room: 0.5

Chair: Maciej Paszynski

474 CTCmodeler: an agent-based framework to simulate pathogen transmission along an inter-individual contact network in a hospital [abstract]
Abstract: Over the last decade, computational modeling has proved a useful tool to simulate and predict nosocomial transmission of pathogens and optimal control measures in healthcare settings. Nosocomial infections are a major public health issue espe-cially since the worldwide increase of antimicrobial resistance worldwide. Here, we present CTCmodeler, a framework that incorporate an agent-based model to simulate pathogen transmission through inter-individual contact in a hospital set-ting. CTCmodeler uses real admission, swab and contact data to deduce its own parameters, simulates individual-mediated transmission across hospital wards and produces weekly incidence estimates. Most earlier hospital models did not take into account the individual heterogeneity of contact patterns. By contrast, CTCmodeler explicitly captures temporal heterogeneous individual contact dy-namics by modelling close proximity interactions over time. Here, we illustrate how CTCmodeler may be used to simulate methicillin-resistant Staphylococcus aureus dissemination in a French long-term care hospital, using longitudinal data on sensor-recorded contacts and weekly swabs from the i-Bird study.
Audrey Duval, David Smith, Didier Guillemot, Lulla Opatowski and Laura Temime
478 Socio-cognitive ACO in Multi-criteria Optimization [abstract]
Abstract: Multi-criteria optimization problems belong to the hardest computational problems tackled, thus metaheuristic-based approach is necessary in order to deal with them. Evolutionary algorithms, swarm intelligence methods and other are very often used in such cases. Based on well-known ``no free lunch theorem'' there is always a need for creating new metaheuristics, though according to Sorensen, they should not be proposed without a firm background. In this paper a socio-cognitive ACO-type algorithm is proposed for multi-criteria TSP problem optimization. This algorithm is rooted in psychological inspirations and follows other socio-cognitive swarm intelligence methods proposed up to now. This paper presents the idea and shows the applicability of the proposed algorithm based on selected benchmark functions from the scope of well-known TSPLIB library.
Aleksander Byrski, Wojciech Turek, Wojciech Radwanski and Marek Kisiel-Dorohinicki
495 Reconfiguration of the multi-channel communication system with hierarchical structure and distributed passive switching [abstract]
Abstract: One of the key problems in parallel processing systems is the architecture of internodal connections, thus affecting the computational efficiency of the whole. In this work authors describe proposition of a new multi-channel hierarchical computational environment with distributed passive switching. According to authors, improvement of communication efficiency should be based on grouping of system components (processing nodes and channels). In the first group, processing nodes are combined into independent groups that communicate using a dedicated channel group. The second type of clustering groups channels available in the system. In particular, they are divided into smaller independent fragments that can be combined into clusters that support selected users. In this work, a model of proposed computational environment and basic reconfiguration protocol were described. The necessary components and management of reconfiguration, passive switching and hierarchization were discussed, highlighting related problems to be solved.
Piotr Hajder and Łukasz Rauch
506 Multi-agent environment for decision support in production system using machine learning methods [abstract]
Abstract: This paper presents a model and implementation of a multi-agent system to support decisions to optimize a configuration of the production process in an company. Our goal is to choose the most desirable parameters of the technological process using computer simulation, which will help to avoid or reduce the number of much more expensive trial production processes, using physical production lines. These identified values of production process parameters will be applied later in the real mass production. Decision-making strategies are selected using different machine learning techniques that assist in obtaining products with the required parameters, taking into account sets of historical data. The focus was primarily on the analysis of the quality of prediction of the obtained product parameters for the different algorithms used and different sizes of historical data sets, and therefore different details of information, and secondly on the examining of the times necessary for building decision models for individual algorithms and data sets. The following algorithms were used: Multilayer Perceptron, Bagging, RandomForest, M5P and Voting. The experiments presented were carried out using data obtained for foundry processes. The JADE platform and the Weka environment were used to implement the multi--agent system.
Jaroslaw Kozlak, Bartlomiej Sniezynski, Dorota Wilk-Kolodziejczyk, Albert Leśniak and Krzysztof Jaśkowiec