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La historia de los avances médicos está ligada al desarrollo de la ingeniería. No en pocas ocasiones, los avances en el conocimiento científico-médico han ido por delante de lo que la tecnología podía ofrecer y ha sido sólo, después de años de desarrollo, cuando se ha podido implementar el instrumento que satisfacía las necesidades concretas. En este artículo se pone de manifiesto la simbiosis entre la ingeniería y la medicina a lo largo de la historia y se centra en los avances significativos que han ocurrido en tres tecnologías clave en los últimos años: la robótica, las comunicaciones móviles 5G y la inteligencia artificial. Todo ello con aplicación, principalmente, al ámbito de la cirugía por ser, quizás, uno de los ámbitos donde ésta resulta más impactante. Además, se proporcionarán las claves fundamentales para poder entender el impacto actual y futuro de estas tecnologías.

With heat waves expected to worsen in the 21st century it is essential to take stock of our current understanding, knowledge gaps, and to set research priorities.

To organise weather data as images, pixels represent coordinates and magnitude of pixels represents the state of the observed variable in a given time. Observed variables, such as air temperature, mean sea level pressure, wind components and others, may be collected into higher dimensional images or even into a motion structure. Codification of formers as a spatial and the latter as a spatio-temporal allows them to be processed using the deep learning methods, for instance autoencoders and autoencoder-like architectures. The objective of the original autoencoder is to reproduce the input image as much as possible, thus effectively equalising the input and output during the training. Then, an advantage of autoencoder can be utilised to calculate the deviations between (1) true states (effectively the inputs), which are derived by nature, and the (2) expected states, which are derived by means of statistical learning. Calculated deviations can then be interpreted to identify the extreme events, such as heatwaves, hot days or any other rare events (so-called anomalies). Additionally, by modelling deviations by statistical distributions, geographical areas with higher probabilities of anomalies can be deduced at the tails of the distribution. The capability of reproduction of the (original input) images is hence crucial in order to avoid addressing arbitrary noise as anomaly. We would like to run experiments to realise the effective architecture that give reasonable solutions, verify the benefits of implementing the variational autoencoder, realise the effect of selecting various statistical loss functions, and find out the effective architecture of the decoder part of the autoencoder.

Extreme event attribution quantifies the influence of climate change on a particular extreme event (EE). Understanding the extent to which climate change is responsible for particular EE is of paramount importance because of the vulnerability of society and ecosystems to these events, especially when it comes to heatwaves that have become more frequent and intense in many parts of the world in recent decades. This led the scientific community to focus its efforts on attribution analysis and the implementation of new techniques for its study. Attribution studies of temperature EE using machine learning (ML) methods are scarce in the specialized literature. Most attribution studies perform statistical comparison between the probability of occurrence of an event today with its probability in the pre-industrial past, making it possible to determine how much more likely that event is due to climate change and how much severe it could be. However, some limitations of these classical methodologies are the difficulty in understanding the links between the physical processes responsible for the occurrence of extreme events and anthropogenic forcing and the impossibility of detecting new trends associated with this forcing. The CLImate INTelligent (CLINT) project aims, among its objectives, to design ML algorithms to improve classical attribution methodologies in some of the aforementioned limitations for three european hot-spots located in Spain, Italy and Netherlands. In this framework, this work presents a preliminary attribution analysis for summer heatwaves focused in Iberian Peninsula and based on deep learning tools such as anomaly detection with autoencoders. The autoencoder is an unsupervised method that comprises two neural networks, one to encode information and the other to decode it. The autoencoder is fed with pre-industrial realizations integrated in the framework of the Coupled Model Intercomparison Project in its sixth version (CMIP6) in such a way that it allows detecting variabilities and trends that are present in the historical run and not in the pre-industrial one. In addition, the influence of climate change for a particular temperature EE could be associated with the AE anomaly for this EE.

Investigating the global connectivities of extreme events is vital for accurate risk reduction and adaptation planning. While human and natural systems have a certain resilience level against single extremes, they may be unable to cope with multiple extreme events whose impacts tend to be amplified in a non-linear relationship. Concurrent droughts and heatwaves are frequently linked to severe damage in socioeconomic sectors such as agriculture, energy, health, and water resources. They can also have detrimental effects on natural ecosystems. Here, we detect global scale dependencies of large-scale droughts and heatwaves using an AI-enhanced point process-based approach, where large-scale events are defined to occur when a certain amount of grid points (e.g., 20%) of a given region of interest experiences heatwave or drought conditions. The classic inhomogeneous and non-stationary J-function can determine whether the occurrence of the events shows clustering, inhibition or independence. However, the analysis and interpretation of this function are usually affected by a high degree of subjectiveness, and its application for large datasets and/or ensembles is challenging. The proposed AI-based automated interpretation tool replaces a subjective and user-dependent approach. Monte Carlo simulations based on standard point process models, reflecting the aforementioned dependence structures, are utilized, allowing the dependence structure to be labeled and the classification problem to be trained using Deep Learning algorithms. To identify the global connectivities of large-scale droughts and heatwaves, we first detect extreme events at the grid scale based on appropriately selected indices. A cluster analysis pinpoints areas with similar drought and heatwave patterns, thus identifying the regions of interest for the large-scale events. For these events we compute the J-functions, and the dependence structure of the large-scale events is then classified by the AI-tool. Links to teleconnections (such as the El Niño-Southern Oscillation and the North Atlantic Oscillation) can be further identified by analyzing the dependencies conditioning on the teleconnection phase under consideration. The proposed tool can be used in diverse research questions where a point process approach is appropriate, and thus has applications beyond climate science.

In this ongoing study we aim at using machine learning algorithms to better understand and improve southern Europe summer heatwave prediction on sub-seasonal to seasonal timescales (S2S). Summer heatwaves are extreme events that have large socio-economic impacts on mortality rate, crop yields, energy demand or water resources and southern Europe is particularly prone and vulnerable to such events. To do this, we train a convolutional network coupled with a multilayer perceptron to forecast with a 15-day and 1-month lead times the occurrence and intensity of heatwave in summer. This forecast model is trained with ERA5 data. The predictors fed to this model are monthly means of the SST, local soil moisture, outgoing longwave radiation, snow cover and sea-ice cover. The target is a monthly-mean heatwave index integrated over a sub-area of southern Europe. Here, we will present the initial results of this ongoing work and the next steps, focusing first on the Iberian Peninsula only.

In the last years, our society's high energy demand has led to the proposal of novel ways of consuming and producing electricity. In this sense, many countries have encouraged micro generation, including the use of renewable sources such as solar irradiation and wind generation, or considering the insertion of electric vehicles as dispatchable units on the grid. This work addresses the Optimal active&-reactive power dispatch (OARPD) problem (a type of optimal power flow (OPF) task) in microgrids considering electric vehicles. We used the modified IEEE 57 and IEEE 118 bus-systems test scenarios, in which thermoelectric generators were replaced by renewable generators. In particular, under the IEEE 118 bus system, electric vehicles were integrated into the grid. To solve the OARDP problem, we proposed the use and improvement of the Canonical Differential Evolutionary Particle Swarm Optimization (C-DEEPSO) algorithm. For further refinement in the search space, C-DEEPSO relies on local search operators. The results indicated that the proposed improved C-DEEPSO was able to show generation savings (in terms ofmillions of dollars) acting as a dispatch controller against two algorithms based on swarm intelligence., European Commission, Agencia Estatal de Investigación, Comunidad de Madrid

In this paper we have tackled the problem of long-term air temperature prediction with eXplainable Artificial Intelligence (XAI) models. Specifically, we have evaluated the performance of an Artificial Neural Network (ANN) architecture with sigmoidal neurons in the hidden layer, trained by means of an evolutionary algorithm (Evolutionary ANNs, EANNs). This XAI model architecture (XAI-EANN) has been applied to the long-term air temperature prediction at different sub-regions of the South of the Iberian Peninsula. In this case, the average August air temperature has been predicted from ERA5 Reanalysis data variables, obtaining good predictions skills and explainable models in terms of the input climatological variables considered. A cluster analysis has been first carried out in terms of the average air temperature in the zone, in such a way that a number of sub-regions with different air temperature behaviour have been defined. The proposed XAI-EANN model architecture has been applied to each of the defined sub-regions, in order to find significant differences among them, which can be explained with the XAI-EANN models obtained. Finally, a comprehensive comparison against some state-of-the-art techniques has also been carried out, concluding that there are statistically significant differences in terms of accuracy in favour of the proposed XAI-EANN model, which also benefits from being an XAI model.

Organic disordered semiconductors have a growing importance because of their low cost, mechanical flexibility, and multiple applications in thermoelectric devices, biosensors, and optoelectronic devices. Carrier transport consists of variable-range hopping between localized quantum states, which are disordered in both space and energy within the Gaussian disorder model. In this paper, we model an organic disordered semiconductor system as a network embedded in both space and energy so that a node represents a localized state while a link encodes the probability (or, equivalently, the Miller–Abrahams hopping rate) for carriers to hop between nodes. The associated network Laplacian matrix allows for the study of carrier dynamics using edge-centric random walks, in which links are activated by the corresponding carrier hopping rates. Our simulation work suggests that at room temperature the network exhibits a strong propensity for small-network nature, a beneficial property that in network science is related to the ease of exchanging information, particles, or energy in many different systems. However, this is not the case at low temperature. Our analysis suggests that there could be a parallelism between the well-known dependence of carrier mobility on temperature and the potential emergence of the small-world property with increasing temperature.

The prediction of convective clouds formation is a very important problem in different areas such as agriculture, natural hazards prevention or transport-related facilities. In this paper, we evaluate the capacity of different types of evolutionary artificial neural networks to predict the formation of convective clouds, tackling the problem as a classification task. We use data from Madrid-Barajas airport, including variables and indices derived from the Madrid-Barajas airport radiosonde station. As objective variable, we use the cloud information contained in the METAR and SPECI meteorological reports from the same airport and we consider a prediction time horizon of 12 h. The performance of different types of evolutionary artificial neural networks has been discussed and analysed, including three types of basis functions (sigmoidal unit, product unit and radial basis function) and two types of models, a mono-objective evolutionary algorithm with two objective functions and a multi-objective evolutionary algorithm optimised by the two objective functions simultaneously. We show that some of the developed neuro-evolutionary models obtain high quality solutions to this problem, due to its high unbalance characteristic.

The authors present a novel self-organized climate regionalization (CR) method that obtains a spatial clustering of regions, based on the explained variance of physical measurements in their coverage. This method enables a microscopic characterization of the probabilistic spatial extent of climate regions, using the statistics of the obtained clusters. It also allows for the study of the macroscopic behaviour of climate regions through time by using the dissimilarity among different cluster size probability histograms. The main advantages of the presented method, based on the Second-Order Data-Coupled Clustering (SODCC) algorithm, are that SODCC is robust to the selection of tunable parameters and that it does not require a regular or homogeneous grid to be applied. Moreover, the SODCC method has higher spatial resolution, lower computational complexity, and allows for a more direct physical interpretation of the outputs than other existing CR methods, such as Empirical Orthogonal Function (EOF) or Rotated Empirical Orthogonal Function (REOF). These facts are illustrated with an example of winter wind speed regionalization in the Iberian Peninsula through the period (1979 − 2014). This study also reveals that the North Atlantic Oscillation (NAO) has a high influence over the wind distribution in the Iberian Peninsula in a subset of years in the considered period.

The Unequal Area Facility Layout Problem (UA-FLP) is a relevant optimization problem related to industrial design, that deals with obtaining the most effective allocation of facilities, that make up the rectangular manufacturing plant layout. The UA-FLP is known to be a hard optimization problem, where meta-heuristic approaches are a good option to obtain competitive solutions. Many of these computational approaches, however, usually fall into local optima, and suffer from lack of diversity in their population, mainly due to the huge search spaces and hard fitness landscapes produced by the traditional representation of UA-FLP. To solve these issues, in this paper we propose a novel hybrid meta-heuristic approach, which combines a Coral Reefs Optimization algorithm (CRO) with a Variable Neighborhood Search (VNS) and a new representation for the problem, called Relaxed Flexible Bay Structure (RFBS), which simplifies the encoding and makes its fitness landscape more affordable. Thus, the use of VNS allows more intensive exploitation of the searching space with an affordable computational cost, as well as the RFBS allows better management of the free space into the plant layout. This combined strategy has been tested over a set of UA-FLP instances of different sizes, which have been previously tackled in the literature with alternative meta-heuristics. The tests results show very good performance in all cases.

In this paper, new probabilistic and dynamic (adaptive) strategies for creating multi-method ensembles based on the coral reef optimization with substrate layers (CRO-SL) algorithm are proposed. CRO-SL is an evolutionary-based ensemble approach that is able to combine different search procedures for a single population. In this work, two different probabilistic strategies to improve the algorithm are analyzed. First, the probabilistic CRO-SL (PCRO-SL) is presented, which substitutes the substrates in the CRO-SL population with tags associated with each individual. Each tag represents a different operator which will modify the individual in the reproduction phase. In each generation of the algorithm, the tags are randomly assigned to the individuals with similar probabilities, obtaining this way an ensemble that sees more intense changes with the application of different operators to a given individual than CRO-SL. Second, the dynamic probabilistic CRO-SL (DPCRO-SL) is presented, in which the probability of tag assignment is modified during the evolution of the algorithm, depending on the quality of the solutions generated in each substrate. Thus, the best substrates in the search process will be assigned higher probabilities than those which showed worse performance during the search. The performances of the proposed probabilistic and dynamic ensembles were tested for different optimization problems, including benchmark functions and a real application of wind-turbine-layout optimization, comparing the results obtained with those of existing algorithms in the literature.

Colloidal quantum dots (CQDs) have been proposed to obtain intermediate band (IB) materials. The IB solar cell can absorb sub-band-gap photons via an isolated IB within the gap, generating extra electron-hole pairs that increase the current without degrading the voltage, as has been demonstrated experimentally for real cells. In this paper, we model the electron hopping transport (HT) as a network embedded in space and energy so that a node represents the first excited electron state localized in a CQD while a link encodes the Miller–Abrahams (MA) hopping rate for the electron to hop from one node (=state) to another, forming an “electron-HT network”. Similarly, we model the hole-HT system as a network so that a node encodes the first hole state localized in a CQD while a link represents the MA hopping rate for the hole to hop between nodes, leading to a “hole-HT network”. The associated network Laplacian matrices allow for studying carrier dynamics in both networks. Our simulations suggest that reducing both the carrier effective mass in the ligand and the inter-dot distance increases HT efficiency. We have found a design constraint: It is necessary for the average barrier height to be larger than the energetic disorder to not degrade intra-band absorption.

The rapid development of computer science and telecommunications has brought new ways and practices to sport training. The artificial sport trainer, founded on computational intelligence algorithms, has gained momentum in the last years. However, artificial sport trainer usually suffers from a lack of automatisation in realization and control phases of the training. In this study, the Digital Twin is proposed as a framework for helping athletes, during realization of training sessions, to make the proper decisions in situations they encounter. The digital twin for artificial sport trainer is based on the cognitive model of humans. This concept has been applied to cycling, where a version of the system on a Raspberry Pi already exists. The results of porting the digital twin on the mentioned platform shows promising potential for its extension to other sport disciplines. Akemi Galvez and Andres Iglesias have received funding from the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement no. 778035, and from the project TIN2017-89275-R funded by MCIN/AEI/10.13039/501100011033/FEDER “Una manera de hacer Europa”.

In this paper we tackle a problem of representative measuring points selection for temperature field reconstruction. This problem is a version of the more general Representative Selection (RS) problem, well-known in computer and data science. In this particular case, the objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a reconstruction error is minimized when reconstructing the monthly average temperature field. We use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer (CRO-SL), which is an evolutionary-type method able to combine several different search procedures within a single population. The CRO-SL is combined with the Analogue Method (AM) to identify the most representative points. This approach exhibits strong performance from experiments with gridded and un-gridded temperature field datasets (European Climate Assessment & Dataset (ECA) and ERA-Interim reanalysis (ERA)). Different aspects such as the error assessment and the comparison with alternative approaches, are discussed in the experimental analysis of this article. We show that the algorithm performs better than a greedy approach, i.e. the best solution for N points is different from the N best individual predictors. The solutions obtained with the proposed methodology are climatologically consistent and include points from Scandinavia, Central and Southern Europe, the Black Sea and Central and South Western Asia as the more representative in the case of the ECA dataset; similar areas are selected for ERA. We have found out that once the number of stations/points goes over a threshold, the improvement in the model is obtained by increasing the density of data in the given zones, instead of adding data from different zones to the algorithm. The method proposed may have direct application in Palaeoclimalogy, where there are a large amount of distributed proxies with scarce information, so the proposed approach could be useful to select the most important ones to reconstruct a desired field., This work has been partially supported by the projects TIN2014-54583-C2-2-R, TIN2017-85887-C2-2-P and PALEOSTRAT (CGL2015-69699-R) of the Spanish Ministerial Commission of Science and Technology (MICYT), and by the Comunidad Autónoma de Madrid, under project number S2013ICE-2933_02.

This paper proposes a novel model to construct quasi scale-free geographically-embedded networks, in which the network’s nodes have been generated by means of a Diffusion Limited Aggregation (DLA) process, and the links connecting a new node i to others are generated using a link radius R l i based on the Euclidean distance. The network is constructed by means of an evolutionary-based algorithm, aiming at minimizing the error between the actual degree distribution of the network under construction and that of the scale free networks, k − γ . Several algorithms for generating scale-free geographically-embedded networks over regular Euclidean lattices can be found in the literature but, to the best of our knowledge, the general case over complex or fractal substrates had not been tackled up until now. Although well-known in very large complex networks (with a huge number of nodes and links), the scale-free property has received much less attention for small geographically-embedded networks, in which the study of networks’ properties is much more difficult. The idea of this work is to evolve the link radii for all the nodes in the network, aiming at finally fulfilling the scale-free property, if possible. Our experimental work shows that the proposed model is actually able to generate quasi scale-free geographically embedded networks in an efficient way. Discussions on the algorithm’s performance to generate scale-free networks, a special encoding to improve the search, and the algorithm’s computational evolution are given in the paper. Alternative possibilities of distribution objective (such as Poissonian, which leads to random geographically-embedded networks) are also tested and discussed in this work.

In this work we propose a method to explore the impact of aeronautical easements in the design of urban wind farms near airports. We provide a comprehensive review of airport easements, their classes, shapes and regulations, along with how and to what extent the different types of easements may affect the prospective installation of wind energy generation facilities near airports. We consider not only the physical easements, called Obstacle Limitation Surfaces (Approach Surface and others), but also the electromagnetic or radio easements associated to Communications, Navigation and Surveillance systems (radars and other air navigation aids). As a practical application, we study different cases of potential deployments of both isolated wind turbines and large wind farms, both around and/or inside four different airports in Spain. We give quantitative guidelines that could be used as a reference in the design process, easing the quick approval by the corresponding national air safety authority.

In this paper we evaluate the performance of several Machine Learning regression techniques in a problem of global solar radiation estimation from geostationary satellite data. Different types of neural networks, Support Vector Regression and Gaussian Processes have been selected as regression techniques to be evaluated, due to their good performance in similar problems in the past. The study area is located in the surroundings of the radiometric station of Toledo, Spain. In order to train the regression techniques considered, one complete year of hourly global solar radiation data is used as the target of the experiments, and different input variables are considered: a cloud index, a clear-sky solar radiation model and several reflectivity values from Meteosat visible images. To assess the results obtained by the Machine Learning algorithms, we have selected as a reference three different physical-based methods, a model based on the Heliosat-2 method (Heliosat-2), the Copernicus Atmosphere Monitoring Service (CAMS) and the SolarGIS model (Soevaluate the performance of Machine Learning regressors when the physical models are included as input variables, in a class of post-processing of these physical approaches. The results obtained show the capacity of Machine Learning regressors to obtain reliable global solar radiation estimation by using satellite measurements.

In this paper, we tackle a problem of frequency assignment in Wi-Fi networks with a novel evolutionary-type algorithm. In this version of the problem, we consider the interferences originated by the access points, and also by the clients and all the 11 available channels in the 2.4 GHz Wi-Fi frequency band. The proposed evolutionary-type algorithm is the Coral Reefs Optimization approach with substrate layer (CRO-SL). It is a recently proposed algorithm, which simulates the processes which occur in real coral reefs, including the reproduction and fight for the space of living corals. This version of the algorithm includes a layer of “substrates” which allows using different search patterns jointly in the algorithm. This way, the CRO-SL is able to apply search patterns such as harmony search, differential evolution, Gaussian-based mutations and other traditional and novel search procedures, including local search algorithms, within a single population of solutions. We show the good performance of the proposed approach in a real case study of Wi-Fi frequency assignment, in the Polytechnic School building of the Universidad de Alcala (Spain), where different realistic scenarios of the problem have been simulated and successfully solved with the CRO-SL algorithm.

The huge amount of data chronologically collected in short periods of time by different devices and technologies is an important challenge in the analysis of times series. This problem has produced the development of new automatic techniques to reduce the number of points in the resulting time series, in order to facilitate their processing and analysis. In this paper, we propose a new modification of a coral reefs optimization algorithm (CRO) to tackle the problem of reducing the size of the time series minimizing the approximation error. The modification includes a memetization procedure (hybridization with a local search procedure) of the standard algorithm to improve its quality when finding a promising solution in a given searching area. The memetization process is applied to the worse individuals of the algorithm at the beginning, and only to the best ones at the end of the algorithm’s convergence, resulting in a dynamical search approach called dynamic memetic CRO (DMCRO). The proposed DMCRO performance is compared in this paper against other state-of-the-art CRO algorithms, such as the standard one, its statistically driven version (SCRO) and two different hybrid versions (HCRO and HSCRO, respectively), and the standard memetic version (MCRO). All the algorithms compared have been tested in 15 time series approximation, collected from different sources, including financial problems, oceanography data, and cardiology signals, among others, showing that the best results are obtained by DMCRO.

Diffusion Limited Aggregation (DLA) and Lindenmayer Systems (L-Systems) are two important fractal structures generators, whose characteristics have been often discussed in the literature. On their own, both DLA and L-Systems have been applied to model very different phenomena related to non-equilibrium growth models and self-similarity patterns generation. This paper introduces hybrid L-Systems–Diffusion Limited Aggregation schemes, as a novel combinative approach to model novel growth structures. The most intuitive possibilities for hybridizing these systems are discussed: using L-Systems to guide DLA simulations, and embedding DLA schemes into L-Systems variables. Different computational simulations illustrate the performance of some of these hybrid L-Systems–DLA schemes. Possibilities for extending the application of this hybrid proposal to alternative research fields are also discussed in the paper.

In this work we present a pattern classification approach coupling the Neighbourhood Component Analysis (NCA) classifier with the Canonical Differential Evolutionary Particle Swarm Optimization (C-DEEPSO). The standard NCA uses the conjugate gradient method to minimize the classification error. Here we propose an approach using the C-DEEPSO instead. In the experimental design, the coupled approach is applied to 20 benchmark data sets, and its performance is compared with the standard NCA using the conjugate gradient. The experimental analysis shows the usage of an evolutionary approach to enhance the performance of a machine learning algorithm can be competitive when compared to well-known iterative optimization techniques, and even outperform them in some problems. A real-world problem classifying cyber-attacks to an industrial control system of gas pipelines is also solved by the proposed approach. The results obtained indicate the proposed approach can successfully identify possible cyber-attacks to the control system. In this way, the NCA coupled to C-DEEPSO can work as an Intrusion Detection Systems (IDS), being able to guarantee an acceptable security level.

The unit dispatch problem is defined as the attribution of operational values to each generation unit inside a hydro-power plant (HPP), given some criteria such as the total power to be generated, or the operational bounds of each unit. An optimal dispatch programming for hydroelectric units in HPP provides a larger production of electricity, with minimal water use. This paper presents an evolutionary approach to optimize the multi-criteria electric dispatch problem in a general HPP, based on a Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm. The proposed approach integrates mathematical models and evolutionary swarm computation. The experimental analysis shows that the proposed MESH algorithm is able to reach competitive results when compared with classical evolutionary algorithms, the NGA-II and SPEA2 basing on ANOVA inference test. Results also show that the proposed MESH is able to save a large amount of water in the energy production process, supplying the requested load, and minimizing blackout risks and generating a profit around $275,000 monthly.

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system ? a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out., European Commission, Agencia Estatal de Investigación, Comunidad de Madrid

According to the No-Free-Lunch Theorem applied to optimization, there is not a superior algorithm when averaged over all types of possible computational problems in a class. However, for specific problems, we can find algorithms which work better than others, and of course, a superior algorithm in terms of performance and computation time. In the last years, there have been an important work in the development of ensemble meta-heuristic approaches to solve hard optimization problems, which have proven to be better than many existing alternative optimization methods. The concept of the ensemble in optimization refers to the use of a combination of algorithms, search strategies, operators or parameter values, to tackle a large set of optimization problems. In the same line, the idea of the ensemble evolutionary methods (EEM), or evolutionary multi-methods, is to develop ensemble approaches based on evolutionary computation which provide better results than the ensemble composites working on their own. This chapter provides a review and classification of the current state of the art on competitive and cooperative EEM applied to optimization problems.