The growth in the use of machine learning in different areas has revealed challenging classification problems that require robust systems. Multiple Classier Systems (MCSs) have attracted interest from researchers as a method that could address such problems. Optimizing the fitness of an MCS improves its, robustness. The lack of an analysis for MCSs from a fitness perspective is identified. To fill this gap, an analytic model from this perspective is derived mathematically by extending the error analysis introduced by Brown and Kuncheva in 2010. The model relates the fitness of an MCS to the average accuracy, positive-diversity, and negative-diversity of the classifiers that constitute the MCS. The model is verified using a statistical analysis of a Monte-Carlo based simulation. This shows the significance of the indicated relationships by the model. This model provides guidelines for developing robust MCSs. It enables the selection of classifiers which compose an MCS with an improved fitness while improving computational cost by avoiding local calculations. The usefulness of the model for designing classification systems is investigated. A new measure consisting of the accuracy and positive-diversity is developed. This measure evaluates fitness while avoiding many calculations compared to the regular measures. A new system (Gadapt) is developed. Gadapt combines machine learning and genetic algorithms to define subsets of the feature space that closely match true class regions. It uses the new measure as a multi-objective criterion for a multi-objective genetic algorithm to identify the MCSs those create the subsets. The design of Gadapt is validated experimentally. The usefulness of the measure and the method of determining the subsets for the performance of Gadapt are examined based on five generated data sets that represent a wide range of problems. The robustness of Gadapt to small amounts of training data is evaluated in comparison with five existing systems on four benchmark data sets. The performance of Gadapt is evaluated in comparison with eleven existing systems on nine benchmark data sets. The analysis of the experiment results supports the validity of the Gadapt design and the outperforming of Gadapt on the existing systems in terms of robustness and performance.

A hybrid method that addresses the design and optimization of long-span steel trusses is presented. By utilizing advancements in present day computing and biologically inspired analysis and design, an effort has been made to automate the process of evolving optimal trusses in an unstructured problem domain. Topology, geometry and sizing optimization of trusses are simultaneously addressed using a three stage methodology. Multi-objective genetic algorithms are used to optimize the member section sizes of truss topologies and geometries. Converting constraints into additional objectives provides a robust algorithm that results in improved convergence to the pareto-optimal set of solutions. In addition, the pareto-curve plotted based on how well the different objectives are satisfied helps in identifying the trade-offs that exist between these objectives, while also providing an efficient way to rank the population of solutions during the search process. A comparison study between multi-objective genetic algorithms, simulated annealing, and reactive taboo search is conducted to evaluate the efficiency of each method with relation to its overall performance, computational expense, sensitivity to initial parameter settings, and repeatability of finding near-global optimal designs. The benefit of using a three stage approach, and also implementing the entire model on parallel computers, is the high level of computational efficiency that is obtained for the entire process and the near-optimal solutions obtained. The overall efficiency and effectiveness of this method has been established by comparing the truss design results obtained using this method on bridge and roof truss benchmark problems with truss designs obtained by other researchers. One of the salient features of thisresearch is the large number of optimal trusses that are produced as the final result. The range of designs available provides the user with the flexibility to select the truss design that best matches their design requirements. By supporting human-computer interactions between these stages, the program also incorporates subjective aesthetic criteria, which assist in producing final designs in consonance with the user's requirements.

A hybrid method that addresses the design and optimization of long-span steel trusses is presented. By utilizing advancements in present day computing and biologically inspired analysis and design, an effort has been made to automate the process of evolving optimal trusses in an unstructured problem domain. Topology, geometry and sizing optimization of trusses are simultaneously addressed using a three stage methodology. Multi-objective genetic algorithms are used to optimize the member section sizes of truss topologies and geometries. Converting constraints into additional objectives provides a robust algorithm that results in improved convergence to the pareto-optimal set of solutions. In addition, the pareto-curve plotted based on how well the different objectives are satisfied helps in identifying the trade-offs that exist between these objectives, while also providing an efficient way to rank the population of solutions during the search process. A comparison study between multi-objective genetic algorithms, simulated annealing, and reactive taboo search is conducted to evaluate the efficiency of each method with relation to its overall performance, computational expense, sensitivity to initial parameter settings, and repeatability of finding near-global optimal designs. The benefit of using a three stage approach, and also implementing the entire model on parallel computers, is the high level of computational efficiency that is obtained for the entire process and the near-optimal solutions obtained. The overall efficiency and effectiveness of this method has been established by comparing the truss design results obtained using this method on bridge and roof truss benchmark problems with truss designs obtained by other researchers. One of the salient features of thisresearch is the large number of optimal trusses that are produced as the final result. The range of designs available provides the user with the flexibility to select the truss design that best matches their design requirements. By supporting human-computer interactions between these stages, the program also incorporates subjective aesthetic criteria, which assist in producing final designs in consonance with the user's requirements.

Submitted by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-04-10T16:15:19Z No. of bitstreams: 2 dscidl.pdf: 3672005 bytes, checksum: 16e2ea719e96351a648acbff70be2fb0 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-04-10T16:15:19Z (GMT). No. of bitstreams: 2 dscidl.pdf: 3672005 bytes, checksum: 16e2ea719e96351a648acbff70be2fb0 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-02-27 / CNPq / Nesse trabalho são desenvolvidos modelos de quantificação de métricas de risco e confiabilidade para sistemas em diferentes etapas do ciclo de vida. Para sistemas na fase de projeto, um Algoritmo Genético Multiobjetivo (MOGA) é combinado à Simulação Discreta de Eventos (DES) a fim de prover configurações não-dominadas com relação à disponibilidade e ao custo. O MOGA + DES proposto incorpora Processos de Renovação Generalizados para modelagem de reparos imperfeitos e também indica o número ótimo de equipes de manutenção. Para a fase operacional é proposto um hibridismo entre MOGA e Inspeção Baseada no Risco para elaboração de planos de inspeção não-dominados em termos de risco e custo que atendem às normas locais. Regressão via Support Vector Machines (SVR) é aplicada nos casos em que a métrica relacionada à confiabilidade (variável resposta) de um sistema operacional é função de variáveis ambientais e operacionais com expressão analítica desconhecida. Otimização via Nuvens de Partículas é combinada à SVR para a seleção simultânea das variáveis explicativas mais relevantes e dos valores dos hiperparâmetros que aparecem no problema de treinamento de SVR. Com o objetivo de avaliar a incerteza relacionada à variável resposta, métodos bootstrap são combinados à SVR para a obtenção de intervalos de confiança e de previsão. São realizados experimentos numéricos e são apresentados exemplos de aplicação no contexto da indústria do petróleo. Os resultados obtidos indicam que os modelos propostos fornecem informações importantes para o planejamento de custos e para a implementação de ações apropriadas a fim de evitar eventos indesejados. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------This work develops models for quantifying risk and reliability-related metrics of systems in different phases of their life cycle. For systems in the design phase, a Multi-Objective Genetic Algorithm (MOGA) is coupled with Discrete Event Simulation (DES) to provide non-dominated configurations with respect to availability and cost. The proposed MOGA + DES incorporates a Generalized Renewal Process to account for imperfect repairs and it also indicates the optimal number of maintenance teams. For the operational phase, a hybridism between MOGA and Risk-Based Inspection is proposed for the elaboration of non-dominated inspection plans in terms of risk and cost that comply with local regulations. Regression via Support Vector Machines (SVR) is applied when the reliability-related metric (response variable) of an operational system is function of a number of environmental and operational variables with unknown analytical relationship. A Particle Swarm Optimization is combined to SVR for the selection of the most relevant variables along with the tuning of the SVR hyperparameters that appear in its training problem. In order to assess the uncertainty related to the response variable, bootstrap methods are coupled with SVR to construct confidence and prediction intervals. Numerical experiments and application examples in the context of oil industry are provided. The obtained results indicate that the proposed frameworks give valuable information for budget planning and for the implementation of proper actions to avoid undesired events.

[ES] El aumento en el uso de combustibles fósiles para la generación de energía ha contribuido significativamente a la crisis del calentamiento global. Diferentes lugares alejados de la infraestructura eléctrica emplean generadores a base de gasolina que aumentan la contaminación ambiental. En este contexto, la introducción masiva de microrredes en la sociedad ha traído oportunidades para la generación de energía de forma distribuida, beneficiando a personas en todo el mundo. Por ejemplo, las microrredes pueden brindar electricidad a poblaciones vulnerables que viven en áreas remotas con acceso limitado a infraestructuras de transmisión y distribución. Además, las microrredes promueven el uso de recursos renovables, reduciendo el impacto ambiental en comparación con los métodos tradicionales de generación de electricidad, como las plantas de energía térmica o las instalaciones nucleares. Además, las microrredes permiten la generación de electricidad a pequeña escala, lo que permite que las familias logren la independencia energética y vendan el exceso de energía a la compañía eléctrica local. Cualquier inversor en una microrred necesita un algoritmo de control para realizar una regulación en bucle cerrado. En este contexto, el control por modos deslizantes de segundo orden es una estrategia de control robusta que ha ganado atención en las aplicaciones de inversores de microrredes. Mediante el uso de este enfoque, el inversor puede lograr un control preciso y rápido, incluso en presencia de incertidumbres y perturbaciones. El uso de estrategias de control robustas mejora la estabilidad y el rendimiento general del sistema de microrredes, asegurando una gestión de energía óptima. El proceso de ajuste es esencial para los algoritmos de control en bucle cerrado, ya que modifica la respuesta del controlador para alcanzar los objetivos de control. La optimización por enjambre de partículas (PSO por sus siglas en inglés) es un eficiente algoritmo de optimización empleado en controladores en lazo cerrado que puede resolver de manera efectiva problemas multi-objetivo formulados en una sola función de costo. Los parámetros de control del inversor de la microrred pueden ser optimizados mediante la utilización de PSO para lograr los objetivos deseados, ajustando de manera eficiente una estrategia de control. Para controladores por modos deslizantes, algunas estrategias de ajuste se basan en técnicas heurísticas. La función de costo única resuelve varios problemas en una microrred, pero existen dificultades cuando diferentes objetivos en un proceso no pueden ser mejorados simultáneamente debido a su relación conflictiva. Estrategias como Algoritmos Genéticos Multi-Objetivo (MOGA por sus siglas en inglés), Evolución Diferencial Multi-Objetivo (MODE por sus siglas en inglés) y Algoritmo Artificial de Ovejas Multi-Objetivo (MOASA por sus siglas en inglés), han demostrado su capacidad para mejorar el rendimiento del inversor mediante la optimización de objetivos conflictivos. Estos algoritmos pueden equilibrar de manera efectiva objetivos como la reducción del tiempo de respuesta y la minimización del sobreimpulso en la señal de salida del inversor. En consecuencia, el rendimiento general y la eficiencia de los inversores de la microrred pueden mejorar. La integración de algoritmos de control multi-objetivo en los inversores de la microrred tiene un gran potencial para abordar los desafíos de gestión de energía y optimizar el rendimiento. Los inversores de la microrred pueden lograr una mayor estabilidad, eficiencia y confiabilidad utilizando técnicas como el control por modos deslizantes de segundo orden y algoritmos de optimización como PSO, MOGA, MODE y MOASA. Al adoptar estos enfoques, se presenta una nueva metodología para un futuro energético más sostenible y resiliente, al tiempo que se mitigan los efectos adversos del calentamiento global causado por el consumo de combustibles fósiles en la generación convencional de energía. / [CA] L'augment en l'ús de combustibles fòssils per a la generació d'energia ha contribuït significativament a la crisi de l'escalfament global. Diferents llocs allunyats de la infraestructura elèctrica empleen generadors a base de gasolina que augmenten la contaminació ambiental. En aquest context, la introducció massiva de microxarxes a la societat ha comportat oportunitats per a la generació d'energia de forma distribuïda, beneficiant persones arreu del món. Per exemple, les microxarxes poden proporcionar electricitat a poblacions vulnerables que viuen en àrees remotes amb accés limitat a infraestructures de transmissió i distribució. A més, les microxarxes promouen l'ús de recursos renovables, reduint l'impacte ambiental en comparació amb els mètodes tradicionals de generació d'electricitat, com les plantes d'energia tèrmica o les instal·lacions nuclears. A més a més, les microxarxes permeten la generació d'electricitat a petita escala, la qual cosa permet que les famílies aconsegueixin la independència energètica i venguen l'excedent d'energia a la companyia elèctrica local. Qualsevol inversor en una microxarxa necessita un algoritme de control per a realitzar una regulació en bucle tancat. En aquest context, el control per modes lliscants de segon ordre és una estratègia de control robusta que ha guanyat atenció en les aplicacions d'inversors de microxarxes. Mitjançant l'ús d'aquest enfocament, l'inversor pot aconseguir un control precís i ràpid, fins i tot en presència d'incerteses i pertorbacions. L'ús d'estratègies de control robustes millora l'estabilitat i el rendiment general del sistema de microxarxes, assegurant una gestió d'energia òptima. El procés d'ajust és essencial pels algoritmes de control en bucle tancat, ja que modifica la resposta del controlador per a aconseguir els objectius de control. L'optimització per enjambre de partícules (PSO per les seues sigles en anglés) és un eficient algoritme d'optimització emprat en controladors en bucle tancat que pot resoldre de manera efectiva problemes multi-objectiu formulats en una sola funció de cost. Els paràmetres de control de l'inversor de la microxarxa poden ser optimitzats mitjançant l'utilització de PSO per a aconseguir els objectius desitjats, ajustant de manera eficient una estratègia de control. Per a controladors per modes lliscants, algunes estratègies d'ajust es basen en tècniques heurístiques. La funció de cost única resol diversos problemes en una microxarxa, però existeixen dificultats quan diferents objectius en un procés no poden ser millorats simultàniament a causa de la seua relació conflictiva. Estratègies com Algorismes Genètics Multi-Objectiu (MOGA per les seues sigles en anglés), Evolució Diferencial Multi-Objectiu (MODE per les seues sigles en anglés) i Algorisme Artificial de Xais Multi-Objectiu (MOASA per les seues sigles en anglés), han demostrat la seua capacitat per a millorar el rendiment de l'inversor mitjançant l'optimització d'objectius conflictius. Aquests algorismes poden equilibrar de manera efectiva objectius com la reducció del temps de resposta i la minimització del sobreguiny a la senyal de sortida de l'inversor. En conseqüència, el rendiment general i l'eficiència dels inversors de la microxarxa poden millorar. La integració d'algorismes de control multi-objectiu en els inversors de la microxarxa té un gran potencial per a abordar els desafiaments de gestió d'energia i optimitzar el rendiment. Els inversors de la microxarxa poden aconseguir una major estabilitat, eficiència i fiabilitat utilitzant tècniques com el control per modes lliscants de segon ordre i algorismes d'optimització com PSO, MOGA, MODE i MOASA. En adoptar aquests enfocaments, es presenta una nova metodologia per a un futur energètic més sostenible i resilient, al mateix temps que es mitiguen els efectes adversos de l'escalfament global causat pel consum de combustibles fòssils en la generació convencional d'energia. / [EN] The increase in fossil fuel usage for power generation has significantly contributed to the global warming crisis. Various remote areas, detached from electrical infrastructure, rely on gasoline-based generators that escalate environmental pollution. In this context, the widespread implementation of microgrids in society has brought forth opportunities for distributed energy generation, benefiting people worldwide. For instance, microgrids can provide electricity to vulnerable populations in remote areas with limited access to transmission and distribution infrastructures. Furthermore, these microgrids advocate for using renewable resources, diminishing environmental impact compared to traditional methods such as thermal power plants or nuclear facilities. Additionally, microgrids enable small-scale electricity generation, empowering families to achieve energy independence and sell surplus energy to local power companies. Any investor in a microgrid requires a closed-loop control algorithm. In this realm, the second-order sliding mode control is a robust strategy garnering attention in microgrid inverter applications. Through this approach, the inverter can achieve precise and rapid control despite uncertainties and disturbances. Using robust control strategies enhances microgrid systems' stability and overall performance, ensuring optimal energy management. Adjustment processes are pivotal for closed-loop control algorithms, modifying the controller's response to meet control objectives. Particle Swarm Optimization (PSO) is an efficient optimization algorithm employed in closed-loop controllers that can effectively solve multi-objective problems formulated in a single cost function. Control parameters of the microgrid inverter can be optimized using PSO to attain desired objectives, efficiently fine-tuning a control strategy. For sliding mode controllers, some adjustment strategies rely on heuristic techniques. While a single cost function resolves various issues within a microgrid, difficulties arise when different objectives in a process cannot be simultaneously improved due to conflicting relationships. Strategies like Multi-Objective Genetic Algorithms (MOGA), Multi-Objective Differential Evolution (MODE), and Multi-Objective Artificial Sheep Algorithm (MOASA) have proven their ability to enhance inverter performance by optimizing conflicting objectives. These algorithms effectively balance objectives like reducing response time and minimizing overshoot in the inverter's output signal. Consequently, the overall performance and efficiency of microgrid inverters can be enhanced. Integrating multi-objective control algorithms into microgrid inverters holds significant potential in addressing energy management challenges and optimizing performance. Microgrid inverters can achieve greater stability, efficiency, and reliability by utilizing second-order sliding mode control and optimization algorithms like PSO, MOGA, MODE, and MOASA. By embracing these approaches, a new methodology emerges for a more sustainable and resilient energy future while mitigating the adverse effects of global warming caused by conventional fossil fuel consumption in power generation. / Gonzales Zurita, ÓO. (2024). Multi-objective Control on Inverter-Based Microgrids [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203120