Browsing by Keyword "Optimization"
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Item Advanced Machine Learning Techniques and Meta-Heuristic Optimization for the Detection of Masquerading Attacks in Social Networks(Universidad de Alcalá, 2015-12-11) Villar-Rodriguez, Esther; Del Ser, Javier; Salcedo-Sanz, SanchoAccording to the report published by the online protection firm Iovation in 2012, cyber fraud ranged from 1 percent of the Internet transactions in North America Africa to a 7 percent in Africa, most of them involving credit card fraud, identity theft, and account takeover or h¼acking attempts. This kind of crime is still growing due to the advantages offered by a non face-to-face channel where a increasing number of unsuspecting victims divulges sensitive information. Interpol classifies these illegal activities into 3 types: • Attacks against computer hardware and software. • Financial crimes and corruption. • Abuse, in the form of grooming or “sexploitation”. Most research efforts have been focused on the target of the crime developing different strategies depending on the casuistic. Thus, for the well-known phising, stored blacklist or crime signals through the text are employed eventually designing adhoc detectors hardly conveyed to other scenarios even if the background is widely shared. Identity theft or masquerading can be described as a criminal activity oriented towards the misuse of those stolen credentials to obtain goods or services by deception. On March 4, 2005, a million of personal and sensitive information such as credit card and social security numbers was collected by White Hat hackers at Seattle University who just surfed the Web for less than 60 minutes by means of the Google search engine. As a consequence they proved the vulnerability and lack of protection with a mere group of sophisticated search terms typed in the engine whose large data warehouse still allowed showing company or government websites data temporarily cached. As aforementioned, platforms to connect distant people in which the interaction is undirected pose a forcible entry for unauthorized thirds who impersonate the licit user in a attempt to go unnoticed with some malicious, not necessarily economic, interests. In fact, the last point in the list above regarding abuses has become a major and a terrible risk along with the bullying being both by means of threats, harassment or even self-incrimination likely to drive someone to suicide, depression or helplessness. California Penal Code Section 528.5 states: “Notwithstanding any other provision of law, any person who knowingly and without consent credibly impersonates another actual person through or on an Internet Web site or by other electronic means for purposes of harming, intimidating, threatening, or defrauding another person is guilty of a public offense punishable pursuant to subdivision [...]”. IV Therefore, impersonation consists of any criminal activity in which someone assumes a false identity and acts as his or her assumed character with intent to get a pecuniary benefit or cause some harm. User profiling, in turn, is the process of harvesting user information in order to construct a rich template with all the advantageous attributes in the field at hand and with specific purposes. User profiling is often employed as a mechanism for recommendation of items or useful information which has not yet considered by the client. Nevertheless, deriving user tendency or preferences can be also exploited to define the inherent behavior and address the problem of impersonation by detecting outliers or strange deviations prone to entail a potential attack. This dissertation is meant to elaborate on impersonation attacks from a profiling perspective, eventually developing a 2-stage environment which consequently embraces 2 levels of privacy intrusion, thus providing the following contributions: • The inference of behavioral patterns from the connection time traces aiming at avoiding the usurpation of more confidential information. When compared to previous approaches, this procedure abstains from impinging on the user privacy by taking over the messages content, since it only relies on time statistics of the user sessions rather than on their content. • The application and subsequent discussion of two selected algorithms for the previous point resolution: – A commonly employed supervised algorithm executed as a binary classifier which thereafter has forced us to figure out a method to deal with the absence of labeled instances representing an identity theft. – And a meta-heuristic algorithm in the search for the most convenient parameters to array the instances within a high dimensional space into properly delimited clusters so as to finally apply an unsupervised clustering algorithm. • The analysis of message content encroaching on more private information but easing the user identification by mining discriminative features by Natural Language Processing (NLP) techniques. As a consequence, the development of a new feature extraction algorithm based on linguistic theories motivated by the massive quantity of features often gathered when it comes to texts. In summary, this dissertation means to go beyond typical, ad-hoc approaches adopted by previous identity theft and authorship attribution research. Specifically it proposes tailored solutions to this particular and extensively studied paradigm with the aim at introducing a generic approach from a profiling view, not tightly bound to a unique application field. In addition technical contributions have been made in the course of the solution formulation intending to optimize familiar methods for a better versatility towards the problem at hand. In summary: this Thesis establishes an encouraging research basis towards unveiling subtle impersonation attacks in Social Networks by means of intelligent learning techniques.Item Algorithm development for night charging electric vehicles optimization in big data applications(2017) Alvaro-Hermana, Roberto; Fraile-Ardanuy, Jesús; Merino, Julia; Tecnalia Research & InnovationIn this paper a night charging method that optimizes the recharging process of electric vehicles (EVs) depending on hourly energy price in a peer to peer (P2P) energy trading system is presented. This algorithm determines how much energy should be recharged in the battery of each EV and the corresponding time slot to do it, avoiding the discontinuities in the charging process and considering the users’ personal mobility constraints.Item Benchmark dataset for the Asymmetric and Clustered Vehicle Routing Problem with Simultaneous Pickup and Deliveries, Variable Costs and Forbidden Paths(2020-04) Osaba, Eneko; QuantumIn this paper, the benchmark dataset for the Asymmetric and Clustered Vehicle Routing Problem with Simultaneous Pickup and Deliveries, Variable Costs and Forbidden Paths is presented (AC-VRP-SPDVCFP). This problem is a specific multi-attribute variant of the well-known Vehicle Routing Problem, and it has been originally built for modelling and solving a real-world newspaper distribution problem with recycling policies. The whole benchmark is composed by 15 instances comprised by 50–100 nodes. For the design of this dataset, real geographical positions have been used, located in the province of Bizkaia, Spain. A deep description of the benchmark is provided in this paper, aiming at extending the details and experimentation given in the paper A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy (Osaba et al.) [1]. The dataset is publicly available for its use and modification.Item Compounding process optimization for recycled materials using machine learning algorithms(2022) Lopez-Garcia, Pedro; Barrenetxea, Xabier; García-Arrieta, Sonia; Sedano, Iñigo; Palenzuela, Luis; Usatorre, Luis; Tecnalia Research & Innovation; POLIMEROS; FACTORY; COMPOSITEThe sustainable manufacturing of goods is one of the factors to minimize natural resource depletion and CO2 emissions. In the last decade a big effort has been done to transition from linear economy to circular economy. This transition requires to implement re-manufacturing processes into the current industrial manufacturing framework, replacing the sourcing of raw materials by re-manufacturing technologies. However, this transition is very challenging since it requires the transformation of the companies and more specially their processes, from traditional to circular. To speed up this transformation, the use of tools provided by the 4th industrial revolution are crucial. In particular, the use of artificial intelligence techniques enables the optimization of the re-manufacturing processes and make those optimizations available to all the stakeholders. This paper presents an optimization system for re-manufacturing of recycled fiber through compounding processes with materials that come from composite waste or end of life of products. The proposed approach has been trained with the data collected from several experiments carried out with a compounding machine under different specifications, fiber reinforcement grades, and output material properties. The system will allow to set up a compounding machine for different types of reinforced plastics needless of setting point experiments. The algorithms have been tested with previously unseen scenarios and they have proved to be efficient for giving the optimal material characteristics.Item Data Driven Performance Prediction in Steel Making(2022-01-18) Boto, Fernando; Murua, Maialen; Gutierrez, Teresa; Casado, Sara; Carrillo, Ana; Arteaga, Asier; Tecnalia Research & Innovation; FACTORY; CIRMETAL; PROMETALThis work presents three data-driven models based on process data, to estimate different indicators related to process performance in a steel production process. The generated models allow the optimization of the process parameters to achieve optimal performance and quality levels. A new approach based on ensembles has been developed with feature selection methods and four state-of-the-art regression approximations (random forest, gradient boosting, xgboost and neural networks). The results show that the proposed approach makes the prediction more stable reducing the variance for all cases, even in one case, slightly reducing the bias. Furthermore, from the four machine learning paradigms presented, random forest is the one with the best results in a quantitative way, obtaining a coefficient of determination of 0.98 as a maximum, depending on the target sub-process.Item Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles(2019-02) Dendaluce Jahnke, Martin; Cosco, Francesco; Novickis, Rihards; Pérez Rastelli, Joshué; Gomez-Garay, Vicente; Tecnalia Research & Innovation; CCAMThe combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Item Fast Real-Time Trajectory Planning Method with 3rd-Order Curve Optimization for Automated Vehicles(IEEE, 2020-09-20) Lattarulo, Ray; Perez, Joshue; CCAMAutomated driving (AD) is one of the fastest-growing tendencies in the Intelligent Transportation Systems (ITS) field with some interesting demonstrations and prototypes. Currently, the main research topics are aligned with vehicle communications, environment recognition, control, and decision-making. A real-time trajectory planning method for Automated vehicles (AVs) is presented in this paper; the contribution is part of AD’s decision-making module. This novel approach uses the properties of the 3er order Bézier curves to generate fast and reliable vehicle trajectories. Online execution and vehicle tracking capacities are considered on the approach. A feasible trajectory is selected based on the criteria: (i) the vehicle must be contained by a collision-free corridor given by an upper decision layer, (ii) the vehicle must be capable to track the generated trajectory, and (iii) the continuity of the path and curvature must be preserved in the joints. Our approach was tested considering a vehicle length (automated bus) of 12 meters. The scenario has the dimension of a real test location with multiple roundabouts.Item Geo-Fence Based Route Tracking Diagnosis Strategy for Energy Prediction Strategies Applied to EV(IEEE, 2019-10) Prieto, P.; Trancho, E.; Arteta, B.; Parra, A.; Coupeau, A.; Cagigas, D.; Ibarra, E.; POWERTRAIN; Tecnalia Research & InnovationNowadays, the shortage of energy and environmental pollution are considered as relevant problems due to the high amount of traditional automotive vehicles with internal combustion engines (ICEs). Electric vehicle (EV) is one of the solutions to localize the energy source and the best choice for saving energy and provide zero emission vehicles. However, their main drawback when compared to conventional vehicles is their limited energy storage capacity, resulting in poor driving ranges. In order to mitigate this issue, the scientific community is extensively researching on energy optimization and prediction strategies to extend the autonomy of EV. In general, such strategies require the knowledge of the route profile, being of capital importance to identify whether the vehicle is on route or not. Considering this, in this paper, a route tracking diagnosis strategy is proposed and tested. The proposed strategy relies on the information provided by the Google Maps API (Application Programming Interface) to calculate the vehicles reference route. Additionally, a Global Positioning System (GPS) device is used to monitor the real vehicle position. The proposed strategy is validated throughout simulation, Driver in the Loop (DiL) test and experimental tests.Item Integration of Real-Intelligence in Energy Management Systems to Enable Holistic Demand Response Optimization in Buildings and Districts(IEEE, 2018-10-18) Romero, Ander; de Agustin-Camacho, Pablo; Tsitsanis, Tasos; Tecnalia Research & Innovation; EDIFICACIÓN DE ENERGÍA POSITIVA; LABORATORIO DE TRANSFORMACIÓN URBANAAlthough multiple trials have been conducted demonstrating that demand side flexibility works and even though technology roll-out progresses significantly fast, the business application of residential and small tertiary demand response has been slow to develop. This paper introduces a holistic demand response optimization framework that enables significant energy costs reduction at the consumer side, while introducing buildings as a major contributor to energy networks' stability in response to network constraints and conditions. The backbone of the solution consists in a modular interoperability and data management framework that enables open standards-based communication along the demand response value chain. The solution is validated in four large-scale pilot sites, incorporating diverse building types, heterogeneous home, building and district energy systems and devices, a variety of energy carriers and spanning diverse climatic conditions, demographic and cultural characteristics.Item A Multi-objective Harmony Search Algorithm for Optimal Energy and Environmental Refurbishment at District Level Scale(Springer Singapore, 2017) Manjarres, Diana; Mabe, Lara; Oregi, Xabat; Landa-Torres, Itziar; Arrizabalaga, Eneko; Del Ser, Javier; Tecnalia Research & Innovation; IA; PLANIFICACIÓN ENERGÉTICANowadays municipalities are facing an increasing commitment regarding the energy and environmental performance of cities and districts. The multiple factors that characterize a district scenario, such as: refurbishment strategies’ selection, combination of passive, active and control measures, the surface to be refurbished and the generation systems to be substituted will highly influence the final impacts of the refurbishment solution. In order to answer this increasing demand and consider all above-mentioned district factors, municipalities need optimisation methods supporting the decision making process at district level scale when defining cost-effective refurbishment scenarios. Furthermore, the optimisation process should enable the evaluation of feasible solutions at district scale taking into account that each district and building has specific boundaries and barriers. Considering these needs, this paper presents a multi-objective approach allowing a simultaneous environmental and economic assessment of refurbishment scenarios at district scale. With the aim at demonstrating the effectiveness of the proposed approach, a real scenario of Gros district in the city of Donostia-San Sebastian (North of Spain) is presented. After analysing the baseline scenario in terms of energy performance, environmental and economic impacts, the multi-objective Harmony Search algorithm has been employed to assess the goal of reducing the environmental impacts in terms of Global Warming Potential (GWP) and minimizing the investment cost obtaining the best ranking of economic and environmental refurbishment scenarios for the Gros district.Item NATURE- AND BIO-INSPIRED OPTIMIZATION: THE GOOD, THE BAD, THE UGLY AND THE HOPEFUL: Lo bueno, lo malo, lo feo y lo esperanzador(2022-03) Molina, Daniel; Poyatos, Javier; Osaba, Eneko; Del Ser, Javier; Herrera, Francisco; Quantum; IANowadays, optimization has become an important issue for industrial systems and product development. From an engineering perspective, optimization implies adjusting or fine-tuning system designs considering one or more performance factors. Unfortunately, for many complex problems there is no optimization technique that can achieve the optimum solution in a reasonable computation time. As a result, the optimization process is often done manually. In recent years a myriad of optimization techniques have appeared, all inspired by phenomena observed in nature, such as behavioral patterns in animals (such as the exploration and search for food, moving, hunting, …), physical and chemical processes [1]. These techniques, often referred to as nature- or bio-inspired optimization algorithms, allow users to optimize a problem without requiring special knowledge about it: they only need to be informed about the fitness function to be optimized, and the mechanisms by which new candidate solutions can be produced. Each algorithm defines how existing solutions can be combined and modified to create new ones in an intelligent way to search for the best solution. Although they cannot guarantee that the optimum solution will be eventually achieved, they can automatically yield good solutions in reasonable computation times. These features make bio-inspired optimization proposals a promising research area and a great alternative to optimize complex processes, as has been already showcased in many real-world problems. In this work we present nature- and bio-inspired optimization from a global perspective. We describe techniques falling in this area, their evolution, how they operate, and why they bridge an important gap not covered by previous optimization techniques. On a critical note, we also give a clear view of the current situation in the area, indicating the positive aspects and issues that should be urgently improved. Considering this critical view, we suggest promising trends that we believe will lead us to a brighter future in nature- and bio-inspired optimization, plenty of successful examples of their application to real-world engineering problems. The manuscript is structured as follows: Section 2 describes bio-inspired optimization and exposes the reasons and advantages that make this area interesting from the scientific and practical points of view (focusing on introducing what they are and why they are useful). In Section 3 we examine the exciting panorama of recent applications in which nature- and bio-inspired optimization has become a central technology (the good), the upsurge of novel metaphors for the design of new proposals that do not lead to innovative solutions (the bad), and poor methodological practices that draw misleading conclusions that must be avoided in this field (the ugly). Finally, Section 4 summarizes the paper and highlights what is next to be done in the area of bio-inspired optimization (the hopeful), especially for engineering applications.Item Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum(2021-07-30) Alonso, Juncal; Orue-Echevarria, Leire; Osaba, Eneko; López Lobo, Jesús; Martinez, Iñigo; Diaz de Arcaya, Josu; Etxaniz, Iñaki; Tecnalia Research & Innovation; HPA; Quantum; IAThe current IT market is more and more dominated by the “cloud continuum”. In the “traditional” cloud, computing resources are typically homogeneous in order to facilitate economies of scale. In contrast, in edge computing, computational resources are widely diverse, commonly with scarce capacities and must be managed very efficiently due to battery constraints or other limitations. A combination of resources and services at the edge (edge computing), in the core (cloud computing), and along the data path (fog computing) is needed through a trusted cloud continuum. This requires novel solutions for the creation, optimization, management, and automatic operation of such infrastructure through new approaches such as infrastructure as code (IaC). In this paper, we analyze how artificial intelligence (AI)-based techniques and tools can enhance the operation of complex applications to support the broad and multi-stage heterogeneity of the infrastructural layer in the “computing continuum” through the enhancement of IaC optimization, IaC self-learning, and IaC self-healing. To this extent, the presented work proposes a set of tools, methods, and techniques for applications’ operators to seamlessly select, combine, configure, and adapt computation resources all along the data path and support the complete service lifecycle covering: (1) optimized distributed application deployment over heterogeneous computing resources; (2) monitoring of execution platforms in real time including continuous control and trust of the infrastructural services; (3) application deployment and adaptation while optimizing the execution; and (4) application self-recovery to avoid compromising situations that may lead to an unexpected failure.Item Performance of Gradient-Based Solutions versus Genetic Algorithms in the Correlation of Thermal Mathematical Models of Spacecrafts(2017-05-24) Anglada, Eva; Martinez-Jimenez, Laura; Garmendia, Iñaki; CIRMETALThe correlation of the thermal mathematical models (TMMs) of spacecrafts with the results of the thermal test is a demanding task in terms of time and effort. Theoretically, it can be automatized by means of optimization techniques, although this is a challenging task. Previous studies have shown the ability of genetic algorithms to perform this task in several cases, although some limitations have been detected. In addition, gradient-based methods, although also presenting some limitations, have provided good solutions in other technical fields. For this reason, the performance of genetic algorithms and gradient-based methods in the correlation of TMMs is discussed in this paper to compare the pros and cons of them. The case of study used in the comparison is a real space instrument flown on board the International Space Station.Item Performance of Optimization Algorithms in the Model Fitting of the Multi-Scale Numerical Simulation of Ductile Iron Solidification(Multidisciplinary Digital Publishing Institute (MDPI), 2020) Anglada, Eva; Meléndez, Antton; Obregón, Alejandro; Villanueva, Ester; Garmendia, IñakiThe use of optimization algorithms to adjust the numerical models with experimental values has been applied in other fields, but the efforts done in metal casting sector are much more limited. The advances in this area may contribute to get metal casting adjusted models in less time improving the confidence in their predictions and contributing to reduce tests at laboratory scale. This work compares the performance of four algorithms (compass search, NEWUOA, genetic algorithm (GA) and particle swarm optimization (PSO)) in the adjustment of the metal casting simulation models. The case study used in the comparison is the multiscale simulation of the hypereutectic ductile iron (SGI) casting solidification. The model fitting criteria is the value of the tensile strength. Four different situations have been studied: model fitting based in 2, 3, 6 and 10 variables. Compass search and PSO have succeeded in reaching the error target in the four cases studied, while NEWUOA and GA have failed in some cases. In the case of the deterministic algorithms, compass search and NEWUOA, the use of a multiple random initial guess has been clearly beneficious.Item REDUCCIÓN DEL IMPACTO DE VEHÍCULOS ELÉCTRICOS A TRAVÉS DE UNA PLATAFORMA DE ECONOMÍA COLABORATIVA(Grupo Tecma Red S.L., 2016) Alvaro-Hermana, Roberto; Merino, Julia; Fraile-Ardanuy, José Jesús; Castaño, SandraEn este trabajo se presenta una nueva forma de reducir el impacto de la recarga de vehículos eléctricos (VE), basado en aplicaciones de economía colaborativa. La propuesta consiste en que VEs con excedente de energía almacenada en sus baterías puedan vender energía a aquellos VEs que requieran recargar sus baterías durante el día y que estén aparcados en la misma zona y a la misma hora. A través del mercado propuesto, es posible reducir significativamente el coste de la recarga a aquellos usuarios que necesitan recargar fuera del horario nocturno (hasta un 70% dependiendo de la situación) y reducir también el impacto de la recarga sobre la red, puesto que dicha recarga se realiza intercambiando la energía entre vehículos aparcados en la misma zona, sin necesidad de estar conectados a la red eléctrica.Item Route tracking diagnosis algorithm for EV energy prediction strategies(2019) Prieto, P.; Trancho, Elena; Arteta, B.; Parra, A.; Coupeau, A.; Cagigas, D.; Ibarra, E.Current pollution issues generated by internal com bustion engine (ICE) based vehicles have lead to their progressive introduction of electrified transport systems. However, their main drawback is their poor autonomy when compared to conventional vehicles. In order to mitigate this issue, the scientific community is extensively researching on energy optimization and prediction strategies to extend the autonomy of electric vehicles (EV). In general, such strategies require the knowledge of the route profile, being of capital importance to identify whether the vehicle is on route or not. Considering this, in this paper, a geo-fence based route tracking diagnosis strategy is proposed and tested. The proposed strategy relies on the information provided by the Google Maps API (Application Programming Interface) to calculate the vehicles reference route. Additionally, a Global Positioning System (GPS) device is used to monitor the real vehicle position. The proposed strategy is validated throughout simulation and experimental tests.Item Shared Self-Consumption Economic Analysis for a Residential Energy Community(IEEE, 2019-09) Alvaro-Hermana, Roberto; Merino, Julia; Fraile-Ardanuy, Jesus; Castano-Solis, Sandra; Jimenez, David; Tecnalia Research & InnovationSelf-consumption is a growing public demand in an energy environment with growing electricity costs and decreasing photovoltaic installation costs. Shared self-consumption is an imperative aspect for bringing self-consumption into Multi-Family Residential Buildings (MRB), where most families live. Nevertheless, current legislation in most countries does not consider shared self-consumption or does not exploit its full potential; such is the case of Spain or Portugal. This paper will present a novel optimization problem for studying the economics of a shared self-consumption installation in a MRB (composed of five family demands, a PV installation and a battery) with the aim of reducing the total bill of the MRB during an entire year. The impact on energy communities of two different types of energy policies is analysed: the remuneration scheme for the surplus energy (net metering, net billing, and exclusive self-consumption policies) and the regulation for shared self-generated energy (demand-dependent, proportional output and no sharing). It is found that the regulation for the sharing energy can be more important that the remuneration scheme, which has been the traditional target of the self-consumption policy.Item Simulation Platform for Coordinated Charging of Electric Vehicles(2015) Díaz de Arcaya, A.; Lázaro, G.; González-González, A.; Sánchez, V.EMERALD is a project funded by the European Commission under the FP7 program focusing on energy use optimization on the integration of the FEVs into the transport and energy infrastructure. Between the objectives of EMERALD, enhanced power demand prediction and power flow support management system uses the power flow demand simulation platform considered in this paper. The power flow demand simulation platform is a software tool that defines the estimation of FEVs power demand according to different conditions as, arrival and departure curves, the estimation of power production based on renewable energy sources and the electricity cost. The tool coordinates scheduling for charging of FEVs in order to minimize the recharging cost, considering the energy balance between the generation and demand powerItem Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level(2019) Manjarres, Diana; Mabe, Lara; Oregi, Xabat; Landa-Torres, Itziar; Tecnalia Research & Innovation; IA; PLANIFICACIÓN ENERGÉTICAEnergy efficiency and environmental performance optimization at the district level are following an upward trend mostly triggered by minimizing the Global Warming Potential (GWP) to 20% by 2020 and 40% by 2030 settled by the European Union (EU) compared with 1990 levels. This paper advances over the state of the art by proposing two novel multi-objective algorithms, named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Harmony Search (MOHS), aimed at achieving cost-effective energy refurbishment scenarios and allowing at district level the decision-making procedure. This challenge is not trivial since the optimisation process must provide feasible solutions for a simultaneous environmental and economic assessment at district scale taking into consideration highly demanding real-based constraints regarding district and buildings’ specific requirements. Consequently, in this paper, a two-stage optimization methodology is proposed in order to reduce the energy demand and fossil fuel consumption with an affordable investment cost at building level and minimize the total payback time while minimizing the GWP at district level. Aimed at demonstrating the effectiveness of the proposed two-stage multi-objective approaches, this work presents simulation results at two real district case studies in Donostia-San Sebastian (Spain) for which up to a 30% of reduction of GWP at district level is obtained for a Payback Time (PT) of 2–3 years.Item Visualization of Numerical Association Rules by Hill Slopes(Springer, 2020-10-27) Fister, Iztok; Fister, Dušan; Iglesias, Andres; Galvez, Akemi; Osaba, Eneko; Del Ser, Javier; Analide, Cesar; Novais, Paulo; Camacho, David; Yin, Hujun; Quantum; IAAssociation Rule Mining belongs to one of the more prominent methods in Data Mining, where relations are looked for among features in a transaction database. Normally, algorithms for Association Rule Mining mine a lot of association rules, from which it is hard to extract knowledge. This paper proposes a new visualization method capable of extracting information hidden in a collection of association rules using numerical attributes, and presenting them in the form inspired by prominent cycling races (i.e., the Tour de France). Similar as in the Tour de France cycling race, where the hill climbers have more chances to win the race when the race contains more hills to overcome, the virtual hill slopes, reflecting a probability of one attribute to be more interesting than the other, help a user to understand the relationships among attributes in a selected association rule. The visualization method was tested on data obtained during the sports training sessions of a professional athlete that were processed by the algorithms for Association Rule Mining using numerical attributes.