Search

The flexoelectric effect, coupling polarization and strain gradient as well as strain and electric field gradients, is universal to dielectrics, but, as compared to piezoelectricity, it is more difficult to harness as it requires field gradients and it is a small-scale effect. This drawback can be overcome by suitably designing multiscale metamaterials made of a non-piezoelectric base material but exhibiting apparent piezoelectricity. We develop a theoretical and computational framework to perform topology optimization of the representative volume element of such metamaterials by accurately modeling the governing equations of flexoelectricity using a Cartesian B-spline method, describing geometry with a level set, and resorting to genetic algorithms for optimization. We consider a multi-objective optimization problem where area fraction competes with each one of the four fundamental piezoelectric functionalities (stress/strain sensor/actuator). We computationally obtain Pareto fronts, and discuss the different geometries depending on the apparent piezoelectric coefficient being optimized. Our results show that optimal material architectures strongly depend on the specific functional property being optimized, and that, except for stress actuators, optimal structures are low-area-fraction lattices. In general, we find competitive estimations of apparent piezoelectricity as compared to reference materials such as quartz and PZT ceramics. This opens the possibility to design devices for sensing, actuation and energy harvesting from a much wider, cheaper and effective class of materials., Peer Reviewed, Postprint (published version)

The primary objective of conventional manufacturing cell formation typically uses grouping efficiency and efficacy measurement to reduce voids and exceptional parts. This objective frequently leads to extreme solutions, such as the persistently significant workload disparity among the manu­facturing cells. It will have a detrimental psychological impact on operators who work in each formed manufacturing cell. The complexity of the problem increases when there is a requirement to finish all parts before the midday break, at which point the formed manufacturing cells can proceed with the following production batch after the break. This research examines the formation of manufacturing cells using two widely recognized intelligent optimization techniques: genetic algorithm (G.A.) and particle swarm optimisation (PSO). The discussed manufacturing system has flexible machines, allowing each part to have multiple production routing options. The optimisation process involved addressing four simultaneous objectives: enhancing the efficiency and efficacy of the manufacturing cells, minimizing the deviation of manufacturing cells working time with the allocated working hours, which is prior to the midday break, and ensuring a balanced workload for the formed manufacturing cells. The optimisation results demonstrate that the G.A. outperforms the PSO method and is capable of providing manufacturing cell formation solutions with an efficiency level of 0.86, efficacy level as high as 0.64, achieving a minimum lateness of only 24 minutes from the completion target before midday break and a maximum difference in workload as low as 49 minutes.

Autonomous Ground Vehicles play a vital role in various industries, necessitating efficient route planning for mission success without human intervention. This study examines two metaheuristic optimization approaches, Genetic Algorithms and Ant Colony Optimization, within the context of route planning for these robotic entities. The comprehensive methodology utilizes the designs of these metaheuristic methods, and a comparative analysis is conducted through simulated experiments in diverse environments. The findings unveil that Genetic Algorithms excel in simple terrains; while Ant Colony Optimization demonstrates robust exploration capabilities in complex environments with obstacles and additional constraints. This underscores the importance of selecting the appropriate metaheuristic based on specific mission requirements and environmental conditions. The research furnishes insights to designers and operators, enabling informed decisions in route planning strategy selection, thereby enhancing the efficiency and safety of autonomous robotic operations across various scenarios., Los Vehículos Autónomos Terrestres son fundamentales para diversas industrias, requiriendo una planificación de rutas eficiente para el éxito de la misión sin intervención humana. Este estudio evalúa dos enfoques de optimización metaheurística, Algoritmos Genéticos y Optimización por Colonias de Hormigas, en el contexto de la planificación de rutas para estas estructuras robóticas. La metodología integral emplea diseños de estos métodos metaheurísticos, y se lleva a cabo un análisis comparativo mediante experimentos simulados en entornos diversos. Los resultados revelan que los Algoritmos Genéticos sobresalen en terrenos simples, mientras que la Optimización por Colonia de Hormigas demuestra capacidades de exploración robustas en entornos complejos con obstáculos y restricciones adicionales. Esto subraya la importancia de seleccionar la metaheurística adecuada basada en requisitos específicos de la misión y condiciones ambientales. La investigación brinda información a diseñadores y operadores, permitiendo tomar decisiones fundamentadas para la selección de estrategias de planificación de rutas, mejorando así la eficiencia y seguridad de las operaciones robóticas autónomas en diversos escenarios

Many-Objective Feature Selection (MOFS) approaches use four or more objectives to determine the relevance of a subset of features in a supervised learning task. As a consequence, MOFS typically returns a large set of non-dominated solutions, which have to be assessed by the data scientist in order to proceed with the final choice. Given the multi-variate nature of the assessment, which may include objectives (e.g., fairness) unrelated to predictive accuracy, this step is often not straightforward and suffers from the lack of existing tools. For instance, it is common to make use of a tabular presentation of the solutions, which provides little information about the trade-offs and the relationships between objectives over the set of solutions. Adopting a GA-based MOFS with six objectives (number of selected features, balanced accuracy, F1-Score, variance inflation factor, statistical parity, and equalised odds) for two feature selection tasks, this paper illustrates the complex challenge of assessing MOFS results and the need for a methodology to aid and justify the final choice of a solution., The project leading to this publication has received funding from the European Union’s Horizon 2020 research and innovation programme (G.A. No. 955895). A. Abelló and B. Bilalli are funded by the Spanish Ministerio de Ciencia e Innovación under project PID2020-117191RBI00, funding scheme AEI/10.13039/501100011033. G. Bontempi is supported by the Service Public de Wallonie Recherche under G.A. No. 2010235–ARIAC by DigitalWallonia4.ai. Computational resources have been provided by the Consortium des Équipements de Calcul Intensif, funded by the Fonds de la Recherche Scientifique de Belgique (G.A. No. 2.5020.11) and Walloon Region., Peer Reviewed, Postprint (published version)

Machine learning (ML) and Deep learning (DL) are increasingly pivotal in the design of advanced metamaterials, seamlessly integrated with material or topology optimization. Their intrinsic capability to predict and interconnect material properties across vast design spaces, often computationally prohibitive for conventional methods, has led to groundbreaking possibilities. This paper introduces an innovative machine learning approach for the optimization of acoustic metamaterials, focusing on Multiresonant Layered Acoustic Metamaterial (MLAM), designed for targeted noise attenuation at low frequencies (below 1000 Hz). This method leverages ML to create a continuous model of the Representative Volume Element (RVE) effective properties essential for evaluating sound transmission loss (STL), and subsequently used to optimize the overall topology configuration for maximum sound attenuation using a Genetic Algorithm (GA). The significance of this methodology lies in its ability to deliver rapid results without compromising accuracy, significantly reducing the computational overhead of complete topology optimization by several orders of magnitude. To demonstrate the versatility and scalability of this approach, it is extended to a more intricate RVE model, characterized by a higher number of parameters, and is optimized using the same strategy. In addition, to underscore the potential of ML techniques in synergy with traditional topology optimization, a comparative analysis is conducted, comparing the outcomes of the proposed method with those obtained through direct numerical simulation (DNS) of the corresponding full 3D MLAM model. This comparative analysis highlights the transformative potential of this combination, particularly when addressing complex topological challenges with significant computational demands, ushering in a new era of metamaterial and component design., The authors acknowledge the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033, Grant CEX2018-000797-S-19-1, TED2021-129413B-C21) for supporting G. Sal-Anglada with the PhD grant PRE2019-088777 under the FPI programme. This research has also been funded by the Ministry of Research and Universities of the Government of Catalonia, through the research grant 2021-PROD-00016 for the project METACOUSTECH. The authors D. Roca and D. Yago are Serra Húnter Fellows., Peer Reviewed, Postprint (published version)

This document pertaining to the final degree project presents the design and ap- plication of a feedback algorithm for a dynamic urban tolling application. This algorithm allows monitoring and reducing traffic congestion and the resulting pollu- tant emissions in a certain time frame and in the center of the city of Barcelona. The developed algorithm is presented as a dynamic pricing system, variable wit- hin the application time frame, intended to be an effective urban policy that can be implemented in Barcelona in the short term. This pricing policy is referred to in the literature as cordon pricing, road pricing or urban tolling, among many others. The term used in this document will be the Catalan translation of the generic term urban tolling, known as urban toll. It should be noted that when we talk about urban tolling generically, it means charging a toll in an urban area. The other terms mentioned refer to specific ways of charging this toll, such as charging a toll whether entering or exiting an area, delimited by an imaginary cordon, or according to the type of road being used, as in the case of highways and other roads. The designed urban toll aims to discourage the use of private vehicles in the defined congested area, promote public transport, and reduce pollutant emissions of nitrogen oxides, NOx, thus improving the quality of urban air. This strategy used as a policy to reduce the use of private vehicles is not the only one that exists. Currently, in Barcelona, several are already applied, such as the ZBE, bus lanes, or pedestrian areas. Numerous studies have concluded that by the year 2050 more than half of the planet will have moved from rural environments to urban areas. Therefore, being able to control congestion and the generation of emissions from private vehicles is a particularly important issue, especially in the case of Barcelona, as it is the European city with the highest density of cars and motorcycles per km2. The approach of this project, Aquest document referent al treball de fi de grau presenta el disseny i l'aplicació d'un algorisme de feedback per una aplicació de peatge dinàmic urbà. Aquest algorisme permet monitorar i reduir la congestió del trànsit i les emissions contaminants derivades d'aquest, en una certa franja horària i al centre de la ciutat de Barcelona. L'algorisme desenvolupat es presenta com un sistema de tarifació dinàmic, variable dins de la franja horària d'aplicació, que pretén ser una política urbana eficaç que es pugui implementar a Barcelona a curt termini. Aquesta política de tarifació s'anomena a la literatura com cordon pricing, road pricing o urban tolling, entre molts altres. El mot que s'emprarà en aquest document serà la traducció al català del terme genèric urban tolling, conegut com a peatge urbà. Cal notar que quan es parla de peatge urbà, genèricament, significa cobrar un peatge en una àrea urbana. Els altres termes mencionats ens parlen de formes concretes de cobrar aquest peatge, com pot ser cobrar un peatge segons si s'entra o se surt d'una àrea, delimitada amb un cordó imaginari, o bé segons el tipus de carretera que s'estigui usant, com pot ser el cas de les autopistes i altres carreteres. El peatge urbà dissenyat té com a objectiu desincentivar l'ús del vehicle privat en l'àrea congestionada definida, fomentar el transport públic i reduir les emissions contaminants dels òxids de nitrogen, NOx, permetent millorar la qualitat de l'aire urbà. Aquesta estratègia emprada com a política per reduir l'ús dels vehicles privats no és l'única que existeix. Actualment, a Barcelona ja se n'apliquen diverses, com és el cas de la ZBE, els carrils per a busos o les zones de vianants. Nombrosos estudis han conclòs que vora l'any 2050 més de la meitat del planeta s'haurà desplaçat des d'entorns rurals cap als àmbits urbans. El fet de poder controlar la congestió i la generació de les emissions dels vehicles privats, és, doncs, un tema d'especial importància, sobretot en el cas

Most of the existing electric vehicles (EVs) charging controls consider power balance regulation in only one community while neglecting their mobility capability. EVs can be considered as mobile batteries and thus deliver and share electricity between multiple building communities, i.e., Community-to-EV-to-Community (C2V2C). Since different communities can have different power regulation needs, it is challenging to consider the various spatio-temporal needs and balance them. This study aims to bridge the research gap by developing a genetic algorithm based advanced C2V2C control to optimize EV charging and electricity delivery between multiple communities. The developed control considers the spatio-temporal power regulating needs of multiple communities and balances these needs based on the users’ requirements via a weighting factor. Using two building communities as case studies, the developed control is compared with a conventional independent control under two control objectives. The results demonstrated the effectiveness of the C2V2C service in reducing the aggregated peak power exchanges with the grid (e.g., by 1 %∼6%) and reducing the total electricity costs (e.g., by up to 282 %) of multiple communities. The impacts of the users’ preferences are also discussed. This study provides a novel and resource-efficient solution for electricity sharing between different buildings. © 2024 The Authors

This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (Formula presented.) and (Formula presented.) evolution strategies (ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints., Correspondence Address: M.H. Moghadam; Smart Industrial Automation, RISE Research Institutes of Sweden, Västerås, Stora Gatan 36, 722 12, Sweden; This work has been funded by Vinnova through the ITEA3 European IVVES ( https://itea3.org/project/ivves.html ) and H2020‐ECSEL European AIDOaRT ( https://www.aidoart.eu/ ) and InSecTT ( https://www.insectt.eu/ ) projects. Furthermore, the project received partially financial support from the SMILE III project financed by Vinnova, FFI, Fordonsstrategisk forskning och innovation under the grant number: 2019‐05871.

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs.

This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (Formula presented.) and (Formula presented.) evolution strategies (ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints., Correspondence Address: M.H. Moghadam; Smart Industrial Automation, RISE Research Institutes of Sweden, Västerås, Stora Gatan 36, 722 12, Sweden; This work has been funded by Vinnova through the ITEA3 European IVVES ( https://itea3.org/project/ivves.html ) and H2020‐ECSEL European AIDOaRT ( https://www.aidoart.eu/ ) and InSecTT ( https://www.insectt.eu/ ) projects. Furthermore, the project received partially financial support from the SMILE III project financed by Vinnova, FFI, Fordonsstrategisk forskning och innovation under the grant number: 2019‐05871.

Most of the existing electric vehicles (EVs) charging controls consider power balance regulation in only one community while neglecting their mobility capability. EVs can be considered as mobile batteries and thus deliver and share electricity between multiple building communities, i.e., Community-to-EV-to-Community (C2V2C). Since different communities can have different power regulation needs, it is challenging to consider the various spatio-temporal needs and balance them. This study aims to bridge the research gap by developing a genetic algorithm based advanced C2V2C control to optimize EV charging and electricity delivery between multiple communities. The developed control considers the spatio-temporal power regulating needs of multiple communities and balances these needs based on the users’ requirements via a weighting factor. Using two building communities as case studies, the developed control is compared with a conventional independent control under two control objectives. The results demonstrated the effectiveness of the C2V2C service in reducing the aggregated peak power exchanges with the grid (e.g., by 1 %∼6%) and reducing the total electricity costs (e.g., by up to 282 %) of multiple communities. The impacts of the users’ preferences are also discussed. This study provides a novel and resource-efficient solution for electricity sharing between different buildings. © 2024 The Authors

The Society of Photo-Optical Instrumentation Engineers (SPIE), Integrated Photonics Platforms III 2024 -- 7 April 2024 through 10 April 2024 -- Strasbourg - -201263, Devices engineered for slowing light, utilizing one-dimensional grating waveguides and fabricated from silicon nitride, often necessitate large footprints to secure the required delay, a consequence of the material’s inherently low refractive index. Our approach employs a genetic algorithm to optimize 100×100nm̂2 etchings on a predetermined grating waveguide topology, allowing for either the selective guidance of peak pulse intensity of the output or the augmentation of true time delay within the identical unit length. Within the chosen predetermined topology, the optimal configuration was identified based on the properties of the signal excitation in the time domain. This approach significantly facilitates the application-specific selection of peak intensity decay rate and time delay behavior within a 1D grating waveguide system. © 2024 SPIE., Scientific and Technical Research Council; TUBITAK, (2210A, 122E566)

Triticale grain, a wheat–rye hybrid, has been reported to comply very well with the requirements for modern brewing adjuncts. In this study, two triticale varieties, in both unmalted and malted forms, were investigated at various ratios in the grist, applying different mashing regimes and concentrations of the commercial enzyme Shearzyme® 500 L with the aim of evaluating their impact on wort production. In order to capture the complex relationships between the input (triticale ratio, enzyme ratio, mashing regime, and triticale variety) and output variables (wort extract content, wort viscosity, and free amino nitrogen (FAN) content in wort), the study aimed to implement the use of artificial neural networks (ANNs) to model the mashing process. Also, a genetic algorithm (GA) was integrated to minimize a specified multi-objective function, optimizing the mashing process represented by the ANN model. Among the solutions on the Pareto front, one notable set of solutions was found with objective function values of 0.0949, 0.0131, and 1.6812 for the three conflicting objectives, respectively. These values represent a trade-off that optimally balances the different aspects of the optimization problem. The optimized input variables had values of 23%, 9%, 1, and 3 for the respective input variables of triticale ratio, enzyme ratio, mashing regime, and triticale variety. The results derived from the ANN model, applying the GA-optimized input values, were 8.65% w/w for wort extract content, 1.52 mPa·s for wort viscosity, and 148.32 mg/L for FAN content in wort. Comparatively, the results conducted from the real laboratory mashing were 8.63% w/w for wort extract content, 1.51 mPa·s for wort viscosity, and 148.88 mg/L for FAN content in wort applying same input values. The presented data from the optimization process using the GA and the subsequent experimental verification on the real mashing process have demonstrated the practical applicability of the proposed approach which c

In this paper, we consider an Internet-of-Things network supported by several satellites and multiple cache-assisted unmanned aerial vehicles (UAVs). Due to the long-distance transmission and detrimental effects from the transmission environment, the latency can be extremely high, especially in the presence of backhaul congestion. Therefore, we formulate an optimisation problem with the aim of minimising the total network latency. To reduce the complexity of the original problem, it is divided into three sub-problems, namely, clustering ground users associated with UAVs, cache placement in UAVs (to support the network in avoiding backhaul congestion), and power allocation for satellites and UAVs. We propose a distributed optimisation method consisting of: a non-cooperative game is designed to obtain the solution to the clustering problem; a genetic algorithm, which is powerful in the scenario of many variables, is employed to obtain the optimal solution to the high-complexity caching problem; and a quick estimation technique is used for power allocation. Additionally, a centralised optimisation method is presented as a benchmark. Simulation results show that although the distributed method leads to network latency of approximately 30% higher than the centralised method, it takes significantly less time to execute and is suitable for systems requiring strict real-time computing constraints. Furthermore, the numerical results prove the efficiency of our methods compared with other conventional ones. IEEE

This research presents a methodology to optimize control forces in buildings, which are subjected to seismic loads. A control system called CLF-MR_1 was developed, which combines a genetic algorithm of non-dominated classification NSGA-II and a control system based on fuzzy logic. The controller was numerically evaluated in a real 96 m high building, in which 6 MR magnetorheological dampers were installed. The structure was subjected to 8 earthquake accelerations with different frequency ranges. The input parameters for the proposed control system were the displacements and velocities of the first floor of the building and the only output parameter was the voltage of the MR devices. The efficiency of CLF-MR_1 was compared with a second controller called CLF-MR_2, which operates using an inference system based on linguistic parameters. Results obtained show that CLF-MR_1 significantly improves the dynamic response of the building, compared to the results obtained with CLF-MR_2 and the uncontrolled condition of the building., Esta pesquisa apresenta uma metodologia para otimizar as forças de controle em edifícios sujeitos a cargas sísmicas. Foi desenvolvido um sistema de controle denominado CLF-MR_1, que combina um algoritmo genético de classificação não dominada NSGA-II e um sistema de controle baseado em lógica difusa. O controlador foi testado numericamente em um edifício real de 96 m de altura, no qual foram instalados 6 amortecedores magnetorheológicos MR. A estrutura foi submetida a 8 acelerações de terremoto com diferentes faixas de frequência. Os parâmetros de entrada para o sistema de controle proposto foram os deslocamentos e as velocidades do primeiro andar do edifício, e a tensão dos dispositivos MR foi definida como o único parâmetro de saída. A eficiência do CLF-MR_1 foi comparada com um segundo controlador chamado CLF-MR_2, que opera usando um sistema de inferência baseado em parâmetros linguísticos. Os resultados obtidos indicam que o CLF-MR_1 melhora significativamente a resposta dinâmica do edifício, em comparação com os resultados obtidos com o CLF-MR_2 e a condição não controlada do edifício., Esta investigación presenta una metodología para optimizar fuerzas de control en edificaciones, las cuales se encuentran sometidas a cargas sísmicas. Se desarrolló un sistema de control llamado CLF-MR_1, el cuál combina un algoritmo genético de clasificación no dominada NSGA-II y un sistema de control basado en lógica difusa. El controlador fue ensayado numéricamente en una edificación real de 96 m de altura, en la cual se instalaron 6 amortiguadores magnetoreológicos MR. La estructura fue sometida a 8 aceleraciones de sismo con diferentes rangos frecuenciales. Los parámetros de entrada para el sistema de control propuesto fueron los desplazamientos y las velocidades del primer piso de la edificación y como único parámetro de salida, se definió el voltaje de los dispositivos MR. La eficiencia del CLF-MR_1 fue comparada con un segundo controlador llamado CLF-MR_2, el cual funciona mediante un sistema de inferencia basado en parámetros lingüísticos. Los resultados obtenidos indican que el CLF-MR_1 mejora significativamente la respuesta dinámica de la edificación, en comparación con los resultados obtenidos con el CLF-MR_2 y con la condición no controlada de la edificación.

Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model's parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improve, Article; Export Date: 02 April 2024; Cited By: 0; Correspondence Address: D. Astiaso Garcia; Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, Italy; email: davide.astiasogarcia@uniroma1.it; CODEN: APEND

This study developed a procedure for rapidly reconstructing a crack profile for calculating the parameters of fracture mechanics such as stress intensity factor with energy release rate (J) and displacement opening crack tip using data from the eddy current sensor. The inverse problem focused on adopting genetic algorithms to solve the direct problem iteratively. The use of the differential probe allows a rapid and precise resolution of the direct problem. The incident field produced by the two coils is determined using the 3D finite element results and the variation of impedance in each coil due to the crack. For the inverse problem, the crack’s surface is considered regular shape in terms of dimensions, and the sensor’s impedance expresses the objective function in terms of the width and length of the crack. The evaluation of the shape function and mesh matrix is made dependent on the iterative process, which makes the reversal procedure computationally lightweight when using genetic algorithms. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.

Most of the existing electric vehicles (EVs) charging controls consider power balance regulation in only one community while neglecting their mobility capability. EVs can be considered as mobile batteries and thus deliver and share electricity between multiple building communities, i.e., Community-to-EV-to-Community (C2V2C). Since different communities can have different power regulation needs, it is challenging to consider the various spatio-temporal needs and balance them. This study aims to bridge the research gap by developing a genetic algorithm based advanced C2V2C control to optimize EV charging and electricity delivery between multiple communities. The developed control considers the spatio-temporal power regulating needs of multiple communities and balances these needs based on the users’ requirements via a weighting factor. Using two building communities as case studies, the developed control is compared with a conventional independent control under two control objectives. The results demonstrated the effectiveness of the C2V2C service in reducing the aggregated peak power exchanges with the grid (e.g., by 1 %∼6%) and reducing the total electricity costs (e.g., by up to 282 %) of multiple communities. The impacts of the users’ preferences are also discussed. This study provides a novel and resource-efficient solution for electricity sharing between different buildings. © 2024 The Authors

This paper introduces the notion of switched neural networks for learning multiple functions under different switching configurations. The neural network structure has adjustable parameters and for each function the state of the parameter vector is determined by a mask vector, 1/0 for active/inactive or +1/-1 for plain/inverted. The optimization problem is to schedule the switching strategy (mask vector) required for each function together with the best parameter vector (weights/biases) minimizing the loss function. This requires a procedure that optimizes a vector containing real and binary values simultaneously to discover commonalities among various functions. Our studies show that a small sized neural network structure with an appropriate switching regime is able to learn multiple functions successfully. During the tests focusing on classification, we considered 2-variable binary functions and all 16 combinations have been chosen as the functions. The regression tests consider four functions of two variables. Our studies showed that simple NN structures are capable of storing multiple information via appropriate switching.

This paper introduces the notion of switched neural networks for learning multiple functions under different switching configurations. The neural network structure has adjustable parameters and for each function the state of the parameter vector is determined by a mask vector, 1/0 for active/inactive or +1/-1 for plain/inverted. The optimization problem is to schedule the switching strategy (mask vector) required for each function together with the best parameter vector (weights/biases) minimizing the loss function. This requires a procedure that optimizes a vector containing real and binary values simultaneously to discover commonalities among various functions. Our studies show that a small sized neural network structure with an appropriate switching regime is able to learn multiple functions successfully. During the tests focusing on classification, we considered 2-variable binary functions and all 16 combinations have been chosen as the functions. The regression tests consider four functions of two variables. Our studies showed that simple NN structures are capable of storing multiple information via appropriate switching.

[Abstract]: Retinal vascular tortuosity is an excessive bending and twisting of the blood vessels in the retina that is associated with numerous health conditions. We propose a novel methodology for the automated assessment of the retinal vascular tortuosity from color fundus images. Our methodology takes into consideration several anatomical factors to weigh the importance of each individual blood vessel. First, we use deep neural networks to produce a robust extraction of the different anatomical structures. Then, the weighting coefficients that are required for the integration of the different anatomical factors are adjusted using evolutionary computation. Finally, the proposed methodology also provides visual representations that explain the contribution of each individual blood vessel to the predicted tortuosity, hence allowing us to understand the decisions of the model. We validate our proposal in a dataset of color fundus images providing a consensus ground truth as well as the annotations of five clinical experts. Our proposal outperforms previous automated methods and offers a performance that is comparable to that of the clinical experts. Therefore, our methodology demonstrates to be a viable alternative for the assessment of the retinal vascular tortuosity. This could facilitate the use of this biomarker in clinical practice and medical research.

A critical reduction in the total harmonic distortion in the output voltage of multilevel inverters can be obtained with an appropriate selection of the angles to switch the power semiconductors at low frequency. Among the methods used to obtain the required sequence of angles, it is possible to recognize both standard and optimized methods, like the Newton–Raphson method or the genetic algorithm, to mention a few. However, implementing the genetic algorithm implies using several configuration parameters, so it is difficult to know if the best option was chosen. This paper implements the Taguchi design of experiments, the Taguchi method, to obtain the appropriate switching angles for an multilevel inverter producing an output voltage of five and seven levels. Experimental results obtained from a laboratory prototype using the computed angles in the MATLAB GA solver demonstrate the superiority of the proposed method.

Background: Intervention planners use logic models to design evidence-based health behavior interventions. Logic models that capture the complexity of health behavior necessitate additional computational techniques to inform decisions with respect to the design of interventions. Objective: Using empirical data from a real intervention, the present paper demonstrates how machine learning can be used together with fuzzy cognitive maps to assist in designing health behavior change interventions. Methods: A modified Real Coded Genetic algorithm was applied on longitudinal data from a real intervention study. The dataset contained information about 15 determinants of fruit intake among 257 adults in the Netherlands. Fuzzy cognitive maps were used to analyze the effect of two hypothetical intervention scenarios designed by domain experts. Results: Simulations showed that the specified hypothetical interventions would have small impact on fruit intake. The results are consistent with the empirical evidence used in this paper. Conclusions: Machine learning together with fuzzy cognitive maps can assist in building health behavior interventions with complex logic models. The testing of hypothetical scenarios may help interventionists finetune the intervention components thus increasing their potential effectiveness., QC 20231227

Time-To-Event (TTE) modeling using survival analysis in industrial settings faces the challenge of premature replacements of machine components, which leads to bias and errors in survival prediction. Typically, TTE survival data contains information about components and if they had failed or not up to a certain time. For failed components, the time is noted, and a failure is referred to as an event. A component that has not failed is denoted as censored. In industrial settings, in contrast to medical settings, there can be considerable uncertainty in an event; a component can be replaced before it fails to prevent operation stops or because maintenance staff believe that the component is faulty. This shows up as “no fault found” in warranty studies, where a significant proportion of replaced components may appear fault-free when tested or inspected after replacement. In this work, we propose an expectation-maximization-like method for discovering such premature replacements in survival data. The method is a two-phase iterative algorithm employing a genetic algorithm in the maximization phase to learn better event assignments on a validation set. The learned labels through iterations are accumulated and averaged to be used to initialize the following expectation phase. The assumption is that the more often the event is selected, the more likely it is to be an actual failure and not a “no fault found”. Experiments on synthesized and simulated data show that the proposed method can correctly detect a significant percentage of premature replacement cases., Som manuscript i avhandling/As manuscript in thesis.

Objective. The current practices of designing neural networks rely heavily on subjective judgment and heuristic steps, often dictated by the level of expertise possessed by architecture designers. To alleviate these challenges and streamline the design process, we propose an automatic method, a novel approach to enhance the optimization of neural network architectures for processing intracranial electroencephalogram (iEEG) data. Approach. We present a genetic algorithm, which optimizes neural network architecture and signal pre-processing parameters for iEEG classification. Main results. Our method improved the macro F1 score of the state-of-the-art model in two independent datasets, from St. Anne's University Hospital (Brno, Czech Republic) and Mayo Clinic (Rochester, MN, USA), from 0.9076 to 0.9673 and from 0.9222 to 0.9400 respectively. Significance. By incorporating principles of evolutionary optimization, our approach reduces the reliance on human intuition and empirical guesswork in architecture design, thus promoting more efficient and effective neural network models. The proposed method achieved significantly improved results when compared to the state-of-the-art benchmark model (McNemar's test, p MUCH LESS-THAN 0.01). The results indicate that neural network architectures designed through machine-based optimization outperform those crafted using the subjective heuristic approach of a human expert. Furthermore, we show that well-designed data preprocessing significantly affects the models' performance.

Objective. The current practices of designing neural networks rely heavily on subjective judgment and heuristic steps, often dictated by the level of expertise possessed by architecture designers. To alleviate these challenges and streamline the design process, we propose an automatic method, a novel approach to enhance the optimization of neural network architectures for processing intracranial electroencephalogram (iEEG) data. Approach. We present a genetic algorithm, which optimizes neural network architecture and signal pre-processing parameters for iEEG classification. Main results. Our method improved the macro F1 score of the state-of-the-art model in two independent datasets, from St. Anne's University Hospital (Brno, Czech Republic) and Mayo Clinic (Rochester, MN, USA), from 0.9076 to 0.9673 and from 0.9222 to 0.9400 respectively. Significance. By incorporating principles of evolutionary optimization, our approach reduces the reliance on human intuition and empirical guesswork in architecture design, thus promoting more efficient and effective neural network models. The proposed method achieved significantly improved results when compared to the state-of-the-art benchmark model (McNemar's test, p MUCH LESS-THAN 0.01). The results indicate that neural network architectures designed through machine-based optimization outperform those crafted using the subjective heuristic approach of a human expert. Furthermore, we show that well-designed data preprocessing significantly affects the models' performance.

This study investigates methods for assessing threats in space. Space services are crucial to both civilian and military capabilities, and a loss of such systems could have severe consequences. Space systems are exposed to various types of threats. To ensure the benefits of space-based applications, protect space assets, improve security, and maintain the space environment, it is crucial to assess threats in space. This thesis focuses on co-orbital antagonistic threats arising from satellites that are capable of performing precision manoeuvres. These satellites could either perform physical attacks or perform operations such as inspection, eavesdropping, or disruption on other satellites. Lambert's problem can be utilised for calculating orbital transfers. By solving the problem iteratively over a range of values of when the transfer is executed and the transfer time, it is possible to detect when a transfer is feasible. This can be used to assess when a satellite can pose a threat to a target. The calculations of orbital transfers are improved by the implementation of a genetic algorithm. The algorithm can solve for both direct transfers to the target and transfers using multiple impulses. Furthermore, a genetic algorithm, called NSGA-II, which can handle multiple objective functions is also analysed. The implemented methods show the potential of being employed to assess threats, especially for direct transfers where a single impulse is executed to transfer to a target. In this case, it is possible to identify threats based on the satellite's $\Delta v$ budget. However, when additional impulses are introduced it becomes more complicated. It is more difficult to estimate when an attack is more likely to commence. The implemented methods show potential, but further research is required in order to develop a robust method to assess co-orbital threats. The conducted analysis has highlighted a few aspects that are crucial for assessing co-orbital threats. Information a, Denna studie undersöker metoder för hotbedöming i rymden. Rymdtjänster är av avgörande betydelse för både civila och militära förmågor och förlusten av sådana system kan leda till allvarliga konsekvenser. Rymdsystem är utsatta för olika typer av hot. För att säkerställa fördelarna med rymdbaserade tillämpningar, skydda rymdresurser, förbättra säkerheten och bevara rymdmiljön är det viktigt att bedöma hot i rymden. Detta examensarbete fokuserar på hot från precisionsmanövrerande satelliter som antingen kan genomföra fysiska attacker eller utföra operationer såsom inspektion, avlyssning eller störning av en annan satellit. Lamberts problem kan användas för att beräkna banmanövrar. Genom att lösa problemet iterativt över olika värden för när manöverna utförs och flygtiden är det möjligt att fastställa när en manöver är genomförbar. Detta kan användas för att bedöma när en satellit kan utgöra ett hot mot en målsatellit. Beräkningarna av banmanövrar förbättras genom implementeringen av en genetisk algoritm. Algoritmen kan lösa både direkta manövrar till målet och manövrar med flera impulser. Dessutom analyseras en genetisk algoritm, kallad NSGA-II, som kan hantera flera målfunktioner. De implementerade metoderna visar potential för att kunna användas för hotbedömning, särskilt för direkta manövrar där en enda impuls används för att ändra banan till målet. I detta fall är det möjligt att identifiera hot baserat på satellitens $\Delta v$-budget. Däremot blir det mer komplicerat när ytterligare impulser introduceras. Det blir svårare att bedöma när en attack sannolikt inleds. De implementerade metoderna visar potential, men ytterligare forskning krävs för att utveckla en robust metod för att bedöma hot från precisionsmanövrerande satelliter. Den genomförda analysen har framhävt några aspekter som är av avgörande betydelse för att utföra en hotbedömning. Information om satellitens $\Delta v$-budget som potentiellt kan utgöra ett hot måste vara tillgänglig. Dessutom är inmätn

Inthischapter,thedescriptionofthemethodcalledGeneralized Extremal Optimization (GEO) is presented, including a brief history, the algorithm, and its application to the inverse radiative transfer problem, for the determination of the optical thickness, single scattering albedo, and diffuse reflectivities in the internal part of the boundary surfaces of one-dimensional homogeneous participating media. The results obtained with a variant of the canonical method, called GEOSubscript v a r var,are compared with those obtained with the GA and SA methods, as well as with a hybridization of the GEO method with the SA method (GEO+SA)., isbn: 978-3-031-43543-0 978-3-031-43544-7, Pages: 107-118

In a parallel EA one can strictly adhere to the generational clock, and wait for all evaluations in a generation to be done. However, this idle time limits the throughput of the algorithm and wastes computational resources. Alternatively, an EA can be made asynchronous parallel. However, EAs using classic recombination and selection operators (GAs) are known to suffer from an evaluation time bias, which also influences the performance of the approach. Model-Based Evolutionary Algorithms (MBEAs) are more scalable than classic GAs by virtue of capturing the structure of a problem in a model. If this model is learned through linkage learning based on the population, the learned model may also capture biases. Thus, if an asynchronous parallel MBEA is also affected by an evaluation time bias, this could result in learned models to be less suited to solving the problem, reducing performance. Therefore, in this work, we study the impact and presence of evaluation time biases on MBEAs in an asynchronous parallelization setting, and compare this to the biases in GAs. We find that a modern MBEA, GOMEA, is unaffected by evaluation time biases, while the more classical MBEA, ECGA, is affected, much like GAs are.

Electric aviation is being developed as a new mode of transportation for the urban areas of the future. This requires urban air space management that considers these aircraft. Flight routes need to be determined that avoid no-fly areas, and minimize flight time, energy consumption and added noise. Yet, no method currently exists for optimizing urban flight routes under multiple conflicting objectives while avoiding three-dimensional restricted areas. In our work, this research gap is overcome by optimizing 3D-routes with the multi-criteria optimization technique called Non-dominated Sorting Genetic Algorithm II. We propose a novel procedure in the optimization process to incorporate geographical representations. Furthermore, we include a seeding procedure for initializing the flight routes and repair methods for invalid flight routes that may arise during the optimization process. We apply the optimization to a case study in Manhattan (New York City) for two different aircraft types, the Lilium Jet (vectored thrust) and the EHANG 184 (wingless multicoptor), under three objectives concerning flight time, energy consumption and added noise. Compared to a least-distance path, flight routes were obtained with maximum improvements of 38% in added noise, 65% in flight time and 52% in energy consumption for the EHANG 184. For the Lilium jet, maximum improvements of 43% in added noise, 47% in flight time and 47% in energy consumption were obtained. Still, the obtained noise addition levels by the aircraft in New York City exceed 5 dB, which is considered as long-term noise annoyance. We illustrated that minimizing the added noise requires high search effort compared to the other two objectives. Upon further analysis of the optimization results, we conclude that the Lilium jet as representative of the eVTOL type vectored thrust is more sensitive to flight route changes than the multicoptor EHANG 184. This information may help in air taxi type choice for a certain region a

Optimal distribution networks reconfiguration is very important in reducing losses and operating costs and in improving networks security, stability and reliability indicators. The work objective is reprogramming an algorithm that improves results obtained previously. Paper presents non-dominated classification genetic algorithm II parts adaptation and reprogramming, with a new coding that, unlike previous authors, avoids modified genetic operators and take better advantage of algorithm's potentialities. In addition, graph theory is used to transform non-feasible individuals and avoid new generations of them, dispensing with annoying mesh checking and reducing the search space and computational burden. Proposal's effectiveness was tested with 33 and 70 bars test distribution systems, showing promising results and better than previous proposals., RESUMEN La reconfiguración óptima de redes de distribución es muy importante en la reducción de pérdidas, costos de operación y en la mejora de la seguridad, estabilidad e indicadores de confiabilidad de estas. El trabajo tiene como objetivo reprogramar un algoritmo, que mejore los resultados obtenidos anteriormente. El artículo presenta la adaptación y reprogramación de partes del algoritmo genético de clasificación no dominada II, con una codificación nueva que, a diferencia de autores precedentes, evita la utilización de operadores genéticos modificados y aprovecha mejor las potencialidades del algoritmo. Se auxilia además de la teoría de grafos, para transformar los individuos no factibles y evitar nuevas generaciones de estos, prescindiendo del molesto chequeo de la malla y reduciendo el espacio de búsqueda y la carga computacional. Su eficacia se probó con los sistemas de distribución de prueba de 33 y 70 barras, mostrando resultados prometedores y mejores que propuestas anteriores.

This article aims to propose a new approach for solving production planning and scheduling in the process industries, in such a way to be adaptable to any manufacturing plant and exploring the use of innovative AI-style technologies. The main contributions of the work are: (i) the design of a specific data format to describe any manufacturing plant (including resources, layout and production recipes), being the input of the method; and (ii) the consideration of limited-capacity production lines with intermediate and final buffers in the optimization. The method involves two stages: the first one corresponds to a deterministic optimization algorithm based on Constraint Programming modelling to solve the JSSP in an ideal scenario with no storage limitation; while the second one is a Genetic Algorithm that only comes into play when the solutions obtained from the first one are infeasible for the available storage, so it is a complementary layer to try to solve the mismatches stochastically., Peer Reviewed, Postprint (author's final draft)

The spectral response of wastewater samples allows, through the use of correlation models, to estimate the pollutant load of the samples in a simple, fast and economical way. However, the accuracy of these models can be affected by alterations in the spectral by external agents such as vibrations or temperature changes. In these cases, approximating the spectral response to trend lines can sometimes provide better estimates, while in other, it is better to work with the original spectral response. This research work proposes a methodology to accurately estimate the pollutant load of wastewater using a hybrid characterization model based on decision trees, which allows, in all cases, to obtain the best possible characterization. This model, based on the analysis of the spectral response, determines which genetic algorithm-based estimation model to make use of: the original spectral response or to the approximation of this to global or individual trend lines for each colour group, to estimate the following parameters: chemical oxygen demand (COD), biochemical oxygen demand at 5 days (BOD5), total suspended solids (TSS), total nitrogen (TN) and total phosphorus (TP) in raw and treated wastewater respectively. The study was conducted on 650 wastewater samples from 43 WWTPs. The results show that the hybrid characterization model provides the best possible fit, achieving an improvement up to 5% in raw wastewater samples, and up to 26.32% in treated wastewater with respect to the use of models that employ point values of the original spectral response, being much more significant in the case of TN.

We propose a personal navigation system (called PNS) which navigates a tourist through multiple destinations efficiently. In our PNS, a tourist can specify multiple destinations with desired arrival/stay time and preference degree. The system calculates the route including part of the destinations satisfying tourist's requirements and navigates him/her. For the above route search problem, we have developed an efficient route search algorithm using a genetic algorithm. We have designed and implemented the PNS as a client-server system so that the portable device users can use the PNS through the Internet. Experiments using general map data and PDAs show that our PNS can calculate a semioptimal route almost in real-time., AINA2004 : The 18th International Conference on Advanced Information Networking and Applications , Mar 29 -31, 2004 , Fukuoka, Japan

One of the important issues in wireless sensor network (WSN) is to k-cover the target sensing field and to extend its lifetime. We propose a method to k-cover the field and maximize the WSN lifetime by moving mobile sensor nodes to appropriate positions for a WSN consisting of both static and mobile sensor nodes which periodically collect environmental information. Our target problem is NP-hard. So, we propose a genetic algorithm (GA) based scheme to find a near optimal solution in practical time. In order to speed up the calculation, we devised a method to check a sufficient condition of k-coverage of the field. For the problem that nodes near the sink node have to forward the data from farther nodes, we make a tree where the amount of communication traffic is balanced among all nodes, and add this tree to the initial candidate solutions of our GAbased algorithm. Through computer simulations, we confirmed that our method achieves much longer k-coverage lifetime than conventional methods for 100 to 300 node WSNs., WiMob2009 : IEEE International Conference on Wireless and Mobile Computing, Networking and Communications , Oct 12-14, 2009 , Marrakech, Morocco

Exploration efficiency of GAs largely depends on parameter values. But, it is hard to manually adjust these values. To cope with this problem, several adaptive GAs which automatically adjust parameters have been proposed. However, most of the existing adaptive GAs can adapt only a few parameters at the same time. Although several adaptive GAs can adapt multiple parameters simultaneously, these algorithms require extremely large computation costs. In this paper, we propose self adaptive island GA (SAIGA) which adapts four parameter values simultaneously while finding a solution to a problem. SAIGA is a kind of island GA, and it adapts parameter values using a similar mechanism to meta-GA. Throughout our evaluation experiments, we confirmed that our algorithm outperforms a simple GA using De Jong's rational parameters, and has performance close to a simple GA using manually tuned parameter values., CEC '03 : IEEE Congress on Evolutionary Computation , Dec 8-12, 2003 , Canberra, Australia

Aiming at fast establishment of a wireless network around a multi-level building in a disaster area, we propose an efficient method to determine the locations of network nodes in the air. Nodes are attached to balloons outside a building and deployed in the air so that the network can be accessed from anywhere in the building. In this paper, we introduce an original radio propagation model for predicting path loss from an outdoor position to a position inside a building. In order to address the three-dimensional deployment problem, the proposed method optimizes an objective function for satisfying two goals: (1) guarantee the coverage: the target space needs to be covered by over a certain percentage by wireless network nodes, (2) minimize the number of network nodes. For solving this problem, we propose an algorithm based on a genetic algorithm. To evaluate the proposed method, we compared our method with three benchmark methods, and the results show that the proposed method requires fewer nodes than other methods., BWCCA2013 : 8th International Conference on Broadband and Wireless Computing, Communication and Applications , Oct 28-30, 2013 , Compiegne, France

In this paper, the authors propose a technique to flexibly implement genetic algorithms (GAs) for various problems on FPGAs. For the purpose, the authors propose a common architecture for GA. The proposed architecture allows designers to easily implement a GA as a hardware circuit consisting of parallel pipelines which execute GA operations. The proposed architecture is scalable to increase the number of parallel pipelines. The architecture is applicable to various problems and allows designers to estimate the size of resulting circuits. The authors give a model for predicting the size of resulting circuits from given parameters. Based on the proposed method, the authors have implemented a tool to facilitate GA circuit design and development. Through experiments using knapsack problem and traveling salesman problem (TSP), the authors show that the FPGA circuits synthesized based on the proposed method run much faster and consume much lower power than software implementation on a PC and the model can predict the size of the resulting circuit accurately enough., FCCM 2006 : 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines , Apr 24-26, 2006 , Napa, CA, USA

Multi-objective genetic algorithms (MOGAs) are approximation techniques to solve multi-objective optimization problems. Since MOGAs search a wide variety of pareto optimal solutions at the same time, MOGAs require large computation power. In order to solve practical sizes of the multi objective optimization problems, it is desirable to design and develop a hardware implementation method for MOGAs with high search efficiency and calculation speed. In this paper, we propose a new method to easily implement MOGAs as high performance hardware circuits. In the proposed method, we adopt simple Minimal Generation Gap (MGG) model as the generation model, because it is easy to be pipelined. In order to preserve diversity of individuals, we need a special selection mechanism such as the niching method which takes large computation time to repeatedly compare superiority among all individuals in the population. In the proposed method, we developed a new selection mechanism which greatly reduces the number of comparisons among individuals, keeping diversity of individuals. Our method also includes a parallel execution architecture based on Island GA which is scalable to the number of concurrent pipelines and effective to keep diversity of individuals. We applied our method to multi-objective Knapsack Problem. As a result, we confirmed that our method has higher search efficiency than existing method., CEC2006 : IEEE International Conference on Evolutionary Computation , Jul 16-21, 2006 , Vancouver, BC, Canada

We investigate a specific truck scheduling problem at cross-docks in the postal service industry on an operational level aiming to maximise the number of duly parcels assuming fixed departure times of the outbound trucks. The inbound gates and the conveyors as means of transportation inside the hub constitute the bottleneck resources. As a novel extension, we propose flexible unloading speeds to efficiently utilise the scarce resources. We formalise the problem with a mixed integer program and explicitly incorporate controllable unloading speeds of the inbound trucks. We determine the computational complexity and develop a genetic algorithm to efficiently solve the problem. Our investigation focuses on both the performance of the genetic algorithm and the applicability of the results in a real-world environment by implementing scheduling policies in a simulation model that considers individual parcel interactions. Based on our experimental results, we can state that especially in problem settings with scarce conveyor capacities, our approach to incorporate controllable unloading speeds has the potential of significantly increasing the number of duly parcels.

This research focuses on the realization of rapid reconfiguration in a cloud manufacturing environment to enable flexible resource scheduling, fulfill the resource potential and respond to various changes. Therefore, this paper first proposes a new cloud and software-defined networking (SDN)-based manufacturing model named software-defined cloud manufacturing (SDCM), which transfers the control logic from automation hard resources to the software. This shift is of significance because the software can function as the “brain” of the manufacturing system and can be easily changed or updated to support fast system reconfiguration, operation, and evolution. Subsequently, edge computing is introduced to complement the cloud with computation and storage capabilities near the end things. Another key issue is to manage the critical network congestion caused by the transmission of a large amount of Internet of Things (IoT) data with different quality of service (QoS) values such as latency. Based on the virtualization and flexible networking ability of the SDCM, we formalize the time-sensitive data traffic control problem of a set of complex manufacturing tasks, considering subtask allocation and data routing path selection. To solve this optimization problem, an approach integrating the genetic algorithm (GA), Dijkstra's shortest path algorithm, and a queuing algorithm is proposed. Results of experiments show that the proposed method can efficiently prevent network congestion and reduce the total communication latency in the SDCM., QC 20220613

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset., CC BY 4.0© 2023 Tech Science Press. All rights reserved.Corresponding Author: Johan Sanmartin Berglund. Email: johan.sanmartin.berglund@bth.seThe authors received no specific funding for this study.

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset. © 2023 Tech Science Press. All rights reserved., SNAC

Due to an increasing incidence rate, the need for new effective methods for diagnosing cancer are of high importance. Studies have shown that circulating tumor cells, CTCs, have great potential as a diagnostic biomarker. Acoustophoresis is a label free method, capable of separating CTCs from red blood cells in a blood sample. In theory, this would enable better CTC detection than the gold standard method of today, and in turn a better way of diagnosing cancer. However, due to inertial forces, the particles are pushed closer to the centerline of the channel in a phenomenon known as the spillover effect. This occurs without the influence of acoustophoresis, and minimizing the effect is a prerequisite for acoustophoretic separation to work at high throughput. The aim of this project was to verify that the spillover effect could be decreased by changing the shape of the fluid splitter, as has been suggested by previous studies. This was done using genetic algorithms—algorithms that utilize the concept of biological evolution in order to reach optimal solutions. The results of the project show that it is indeed possible to push the particles further away from the center of the channel, however at the expense of raising the shear stress in the system. The best achieved result was a 40 percent increase in the distance from the center line. Provided that a physically fabricated version of the optimized chip works as well as the simulations showed, this would make the acoustophoresis method more clinically viable.

As fractional-order models increasingly appear as an option to describe complex systems, they generate a demand for parameter estimation methods in the time and frequency domain. The extended Kalman filter (EKF) is a promising technique in the time domain, but it is sensitive to the initial conditions of the state and error covariance matrices. In the case of integer-order systems, evolutionary algorithms (EAs) can tackle EKF's sensitiveness issues. The algorithm usually uses EAs to optimise the initial conditions for the EK, leading to a better estimate of the system parameters and states. Here, we extend this methodology to fractional-order models to estimate the model's fractional order and parameters. Finally, we demonstrate the effectiveness of this methodology on a simple mechanical model., Part of ISBN 9798350321685QC 20230821

The usage of multi-objective cost functions (MOCFs) in sizing and energy management strategy (EMS) of fuel cell hybrid electric vehicles (FCHEVs) has expanded due to the participation of multiple technological and economic disciplines. To better understand the impact of price fluctuation on the component size and EMS of an FCHEV, this article proposed a sensitivity analysis methodology. First, a two-step optimization approach that considers hydrogen consumption, system degradation, and trip cost is used to minimize a MOCF of the Can-Am Spyder electric motorcycle simulator. Then, an effect analysis is carried out for the cost-optimal results under two driving profiles to understand the link between cost variation and system performance. These simulations indicate that each might result in different system sizes and EMS compromise. After that, an online optimization EMS based on sequential quadratic programming is used on a reduced-scale hardware-in-the-loop configuration to evaluate the simulation results with varied weights. Experimental results indicate that when an adequate size is used for each pair of weights, the EMS results in a 6% decrease in the trip cost.

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset. © 2023 Tech Science Press. All rights reserved., SNAC

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset., CC BY 4.0© 2023 Tech Science Press. All rights reserved.Corresponding Author: Johan Sanmartin Berglund. Email: johan.sanmartin.berglund@bth.seThe authors received no specific funding for this study.

Purpose: We consider the problem of route planning of multiple rechargeable heterogeneous UAVs with multiple trips under mission time and payload carrying constraints. The goal is to determine the types and number of UAVs to be deployed and their flying paths that minimizes the monetary cost, which is a sum of the recharging energy cost of each UAV, the UAV rental cost, and the cost of violating the mission time deadline. Design/methodology/approach: The problem is formulated as a mixed integer programming (MIP). Then, the genetic algorithm (GA) is developed to solve the model and the solutions are compared to those obtained from the exact method (Branch-and-Bound). Novel chromosome encoding and population initializations are designed, and standard procedures for crossover and mutation are adapted to this work. Test problems on grid networks and real terrains are used to evaluate the runtime efficiency and solution optimality, and the sensitivity of GA parameters is studied based on two-level factorial experiments. Findings: The proposed GA method can find optimal solutions for small problem sizes but with much less computation time than the exact method. For larger problem sizes, the exact method failed to find optimal solutions within the limits of time and disk space constraints (24 hours and 500 GB) while the GA method yields the solutions within a few minutes with as high as 49% better objective values. Also, the proposed GA method is shown to well explore the solution space based on the variation of the total costs obtained. Originality/value: The unique aspects of this work are that the model optimizes the sum of three different costs – the electricity recharging cost, the UAV rental cost, the penalty cost for mission deadline violation, and the recharging period based on the remaining energy, the payload capacity, and the heterogeneity of UAVs are incorporated into the model. The model is formulated as a mixed integer programming and the genetic algorithm is, Peer Reviewed