L'étude des bactéries pathogènes opportunistes d'origine environnementale ayant des modes de vie variés, libre et autonome ou contraint à une niche spécifique représentée par l'hôte, présente un intérêt dans la compréhension de l'adaptation des bactéries à leurs hôtes et de l'apparition de nouveaux pathogènes. Le genre Aeromonas regroupe des bactéries communes des milieux aquatiques, principalement des eaux douces. Elles sont capables d'entretenir différents types de relations avec leurs hôtes (parasitisme/symbiose) et peuvent être hébergées par un large spectre d'organismes. Chez l'homme, elles sont la cause d'une large variété d'infections (gastroentérite, bactériémie, infection de la peau et des tissus mous, etc.) mais les difficultés d'identification des souches et une taxonomie confuse engendrent une méconnaissance de la pathogénicité réelle des différentes espèces décrites.Le but de ce travail était d'étudier les mécanismes d'évolution génomique et génétique à l'origine de la remarquable capacité d'adaptation des Aeromonas à leurs hôtes, notamment à l'homme. Une analyse comparative de la diversité génétique et génomique d'une large collection de 195 souches représentative des différentes espèces du genre et d'origines variées (humaine, animale et environnementale) a été menée. La diversité génétique a été appréhendée au moyen d'une approche multilocus incluant l'étude des séquences de 7 gènes de ménage (dnaK, gltA, gyrB, radA, rpoB, tsf, zipA). En parallèle, nous avons étudié la variabilité des copies multiples du gène rrs en explorant leur diversité génétique par une méthode d'électrophorèse en condition dénaturante (PCR-TTGE) et la variabilité du nombre et de la répartition des opérons rrn dans le chromosome de ces bactéries par électrophorèse en champ pulsé.Ces différentes approches nous ont permis de mettre en évidence : i) une diversité très élevée des 7 gènes de ménage analysés ainsi que l'existence de transferts latéraux, ii) l'existence de sous-groupes de souches adaptées à un hôte ou à une localisation anatomique particulière, iii) un nombre important d'opérons rrn (8 à 11), iv) l'existence de profils de distribution chromosomique des opérons rrn spécifique d'espèce ou de groupes d'espèces proches, v) une forte proportion (41,5%) des souches présentant une hétérogénéité de séquences des différentes copies du gène rrs. Nos résultats montrent également la valeur taxonomique de l'étude de la diversité génétique et génomique à l'aide des approches proposées au sein du genre Aeromonas.Nous montrons que : i) l'ARN ribosomique 16S est un marqueur informatif pour étudier les modes d'évolution et conduire des études de taxonomie mixte et consensuelle dans le genre Aeromonas à condition d'étudier la diversité de ses multiples copies, ii) A. caviae présente des caractéristiques génétiques particulières témoignant d'un processus d'adaptation en cours à une niche écologique que nous supposons être l'intestin humain. Nos résultats supportent également un mode d'évolution des bactéries du genre Aeromonas dit en complexes d'espèces accompagné de phénomènes de spéciation pouvant en partie expliquer les difficultés rencontrées pour établir une taxonomie claire du genre Aeromonas. / Abstract :Studying opportunistic pathogenic bacteria with an environmental origin and a wide variety of lifestyles, either free-living or host-adapted, is useful to improve the understanding of bacterial adaptation to hosts and the emergence of novel pathogens. The genus Aeromonas groups water-living bacteria, mainly in freshwater. They are able to support several types of relations with their hosts (parasitism/ symbiosis) and are harbored by a large spectrum of hosts. In human, they are involved in a wide range of infections (gastroenteritis, bacteraemia, wound and soft tissue infection, etc.) but difficulties in identifying strains and a confused taxonomy results in incomplete knowledge of the real strain pathogenicity of each described species.The aim of this work was to study the mechanisms of genomic and genetic evolution related to the outstanding ability of Aeromonas adaptation to host, including human. We led a comparative analysis of the genetic and genomic diversity on a large strain collection (195 strains) representative of the species of the genus and from various sources (human, animal, environmental). We studied the genetic diversity using a 7 housekeeping gene multilocus strain analysis (dnaK, gltA, gyrB, radA, rpoB, tsf, zipA). We also described the variability in the i) rrs multiple gene copies using a PRC-TTGE method and ii) the number and distribution of the rrn operons within the chromosome using a pulse field gel electrophoresis. Our results also showed the taxonomic value of the study of genetic and genomic diversity using the approaches proposed in the genus Aeromonas.These various approaches enabled us to highlight: i) a high genetic diversity in the housekeeping genes together with horizontal gene transfers events, ii) some clusters that were either host-adapted or adapted to particular anatomical locations, iii) a high number of rrn operons (from 8 to 11), iv) the presence of patterns of rrn operon that were either species-specific or specific to groups of closely related species, v) a high frequency (41,5%) of strains harboring sequence heterogeneities between rrs copies. We showed that: i) 16 rRNA is a valuable marker for studying the modes of evolution of aeromonads and the taxonomy within the genus Aeromonas provided that multiple copy diversity is taken into account, ii) A. caviae displays particular genetic characteristic that suggested an ongoing process of adaptation to a niche that we supposed to be human digestive tract. Our results also support an evolution mode in complex of species with some speciation process that could at least in part explain difficulties for determining a clarified taxonomy within the genus Aeromonas.

Most of the real world problems in water resources involve nonlinear formulations in their solution construction. Obtaining optimal solutions for large scale nonlinear optimization problems is always a challenging task. The conventional methods, such as linear programming (LP), dynamic programming (DP) and nonlinear programming (NLP) may often face problems in solving them. Recently, there has been an increasing interest in biologically motivated adaptive systems for solving real world optimization problems. The multi-member, stochastic approach followed in Evolutionary Algorithms (EA) makes them less susceptible to getting trapped at local optimal solutions, and they can search easier for global optimal solutions. In this thesis, efficient optimization techniques based on swarm intelligence and evolutionary computation principles have been proposed for single and multi-objective optimization in water resource systems. To overcome the inherent limitations of conventional optimization techniques, meta-heuristic techniques like ant colony optimization (ACO), particle swarm optimization (PSO) and differential evolution (DE) approaches are developed for single and multi-objective optimization. These methods are then applied to few case studies in planning and operation of reservoir systems in India. First a methodology based on ant colony optimization (ACO) principles is investigated for reservoir operation. The utility of the ACO technique for obtaining optimal solutions is explored for large scale nonlinear optimization problems, by solving a reservoir operation problem for monthly operation over a long-time horizon of 36 years. It is found that this methodology relaxes the over-year storage constraints and provides efficient operating policy that can be implemented over a long period of time. By using ACO technique for reservoir operation problems, some of the limitations of traditional nonlinear optimization methods are surmounted and thus the performance of the reservoir system is improved. To achieve faster optimization in water resource systems, a novel technique based on swarm intelligence, namely particle swarm optimization (PSO) has been proposed. In general, PSO has distinctly faster convergence towards global optimal solutions for numerical optimization. However, it is found that the technique has the problem of getting trapped to local optima while solving real world complex problems. To overcome such drawbacks, the standard particle swarm optimization technique has been further improved by incorporating a novel elitist-mutation (EM) mechanism into the algorithm. This strategy provides proper exploration and exploitation throughout the iterations. The improvement is demonstrated by applying it to a multi-purpose single reservoir problem and also to a multi reservoir system. The results showed robust performance of the EM-PSO approach in yielding global optimal solutions. Most of the practical problems in water resources are not only nonlinear in their formulations but are also multi-objective in nature. For multi-objective optimization, generating feasible efficient Pareto-optimal solutions is always a complicated task. In the past, many attempts with various conventional approaches were made to solve water resources problems and some of them are reported as successful. However, in using the conventional linear programming (LP) and nonlinear programming (NLP) methods, they usually involve essential approximations, especially while dealing withdiscontinuous, non-differentiable, non-convex and multi-objective functions. Most of these methods consider multiple objective functions using weighted approach or constrained approach without considering all the objectives simultaneously. Also, the conventional approaches use a point-by-point search approach, in which the outcome of these methods is a single optimal solution. So they may require a large number of simulation runs to arrive at a good Pareto optimal front. One of the major goals in multi-objective optimization is to find a set of well distributed optimal solutions along the true Pareto optimal front. The classical optimization methods often fail to attain a good and true Pareto optimal front due to accretion of the above problems. To overcome such drawbacks of the classical methods, there has recently been an increasing interest in evolutionary computation methods for solving real world multi-objective problems. In this thesis, some novel approaches for multi-objective optimization are developed based on swarm intelligence and evolutionary computation principles. By incorporating Pareto optimality principles into particle swarm optimization algorithm, a novel approach for multi-objective optimization has been developed. To obtain efficient Pareto-frontiers, along with proper selection scheme and diversity preserving mechanisms, an efficient elitist mutation strategy is proposed. The developed elitist-mutated multi-objective particle swarm optimization (EM-MOPSO) technique is tested for various numerical test problems and engineering design problems. It is found that the EM-MOPSO algorithm resulting in improved performance over a state-of-the-art multi-objective evolutionary algorithm (MOEA). The utility of EM-MOPSO technique for water resources optimization is demonstrated through application to a case study, to obtain optimal trade-off solutions to a reservoir operation problem. Through multi-objective analysis for reservoir operation policies, it is found that the technique can offer wide range of efficient alternatives along with flexibility to the decision maker. In general, most of the water resources optimization problems involve interdependence relations among the various decision variables. By using differential evolution (DE) scheme, which has a proven ability of effective handling of this kind of interdependence relationships, an efficient multi-objective solver, namely multi-objective differential evolution (MODE) is proposed. The single objective differential evolution algorithm is extended to multi-objective optimization by integrating various operators like, Pareto-optimality, non-dominated sorting, an efficient selection strategy, crowding distance operator for maintaining diversity, an external elite archive for storing non- dominated solutions and an effective constraint handling scheme. First, different variations of DE approaches for multi-objective optimization are evaluated through several benchmark test problems for numerical optimization. The developed MODE algorithm showed improved performance over a standard MOEA, namely non-dominated sorting genetic algorithm–II (NSGA-II). Then MODE is applied to a case study of Hirakud reservoir operation problem to derive operational tradeoffs in the reservoir system optimization. It is found that MODE is achieving robust performance in evaluation for the water resources problem, and that the interdependence relationships among the decision variables can be effectively modeled using differential evolution operators. For optimal utilization of scarce water resources, an integrated operational model is developed for reservoir operation for irrigation of multiple crops. The model integrates the dynamics associated with the water released from a reservoir to the actual water utilized by the crops at farm level. It also takes into account the non-linear relationship of root growth, soil heterogeneity, soil moisture dynamics for multiple crops and yield response to water deficit at various growth stages of the crops. Two types of objective functions are evaluated for the model by applying to a case study of Malaprabha reservoir project. It is found that both the cropping area and economic benefits from the crops need to be accounted for in the objective function. In this connection, a multi-objective frame work is developed and solved using the MODE algorithm to derive simultaneous policies for irrigation cropping pattern and reservoir operation. It is found that the proposed frame work can provide effective and flexible policies for decision maker aiming at maximization of overall benefits from the irrigation system. For efficient management of water resources projects, there is always a great necessity to accurately forecast the hydrologic variables. To handle uncertain behavior of hydrologic variables, soft computing based artificial neural networks (ANNs) and fuzzy inference system (FIS) models are proposed for reservoir inflow forecasting. The forecast models are developed using large scale climate inputs like indices of El-Nino Southern Oscialltion (ENSO), past information on rainfall in the catchment area and inflows into the reservoir. In this purpose, back propagation neural network (BPNN), hybrid particle swarm optimization trained neural network (PSONN) and adaptive network fuzzy inference system (ANFIS) models have been developed. The developed models are applied for forecasting inflows into the Malaprabha reservoir. The performances of these models are evaluated using standard performance measures and it is found that the hybrid PSONN model is performing better than BPNN and ANFIS models. Finally by adopting PSONN model for inflow forecasting and EMPSO technique for solving the reservoir operation model, the practical utility of the different models developed in the thesis are demonstrated through application to a real time reservoir operation problem. The developed methodologies can certainly help in better planning and operation of the scarce water resources.

[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