<ul> <li>Energy Optimization Stategies</li> <li>Hydraulic Models for Water Distribution Systems</li> <li>Heuristic Multi-objective Optimization Algorithms</li> <li>Multi-objective Optimization Problems</li> <li>System Constraints</li> <li>Encoding Techniques</li> <li>Optimal Pumping Operations</li> <li>Sovling Real-World Optimization Problems </li> </ul> / <p>The water supply industry is a very important element of a modern economy; it represents a key element of urban infrastructure and is an integral part of our modern civilization. Billions of dollars per annum are spent internationally in pumping operations in rural water distribution systems to treat and reliably transport water from source to consumers.</p> <p>In this dissertation, a new multi-objective optimization approach referred to as energy optimization strategy is proposed for minimizing electrical energy consumption for pumping, the cost, pumps maintenance cost, and the cost of maximum power peak, while optimizing water quality and operational reliability in rural water distribution systems. Minimizing the energy cost problem considers the electrical energy consumed for regular operation and the cost of maximum power peak. Optimizing operational reliability is based on the ability of the network to provide service in case of abnormal events (e.g., network failure or fire) by considering and managing reservoir levels. Minimizing pumping costs also involves consideration of network and pump maintenance cost that is imputed by the number of pump switches. Water quality optimization is achieved through the consideration of chlorine residual during water transportation.</p> <p>An Adaptive Parallel Clustering-based Multi-objective Particle Swarm Optimization (APC-MOPSO) algorithm that combines the existing and new concept of Pareto-front, operating-mode specification, selecting-best-efficiency-point technique, searching-for-gaps method, and modified K-Means clustering has been proposed. APC-MOPSO is employed to optimize the above-mentioned set of multiple objectives in operating rural water distribution systems.</p> <p>Saskatoon West is, a rural water distribution system, owned and operated by Sask-Water (i.e., is a statutory Crown Corporation providing water, wastewater and related services to municipal, industrial, government, and domestic customers in the province of Saskatchewan). It is used to provide water to the city of Saskatoon and surrounding communities. The system has six main components: (1) the pumping stations, namely Queen Elizabeth and Aurora; (2) The raw water pipeline from QE to Agrium area; (3) the treatment plant located within the Village of Vanscoy; (4) the raw water pipeline serving four major consumers, including PCS Cogen, PCS Cory, Corman Park, and Agrium; (5) the treated water pipeline serving a domestic community of Village of Vanscoy; and (6) the large Agrium community storage reservoir.</p> <p>In this dissertation, the Saskatoon West WDS is chosen to implement the proposed energy optimization strategy. Given the data supplied by Sask-Warer, the scope of this application has resulted in savings of approximately 7 to 14% in energy costs without adversely affecting the infrastructure of the system as well as maintaining the same level of service provided to the Sask-Water’s clients.</p> <p>The implementation of the energy optimization strategy on the Saskatoon West WDS over 168 hour (i.e., one-week optimization period of time) resulted in savings of approximately 10% in electrical energy cost and 4% in the cost of maximum power peak. Moreover, the results showed that the pumping reliability is improved by 3.5% (i.e., improving its efficiency, head pressure, and flow rate). A case study is used to demonstrate the effectiveness of the multi-objective formulations and the solution methodologies, including the formulation of the system-operational optimization problem as five objective functions. Beside the reduction in the energy costs, water quality, network reliability, and pumping characterization are all concurrently enhanced as shown in the collected results. The benefits of using the proposed energy optimization strategy as replacement for many existing optimization methods are also demonstrated.</p> / Doctor of Science (PhD)

Tracked vehicles are designed to operate in various terrains, ranging from soft mud to hard tarmac. This wide range of terrains presents significant challenges for the suspension system, as its components must be suitable for all types of terrain. The selection of these components is crucial for minimizing acceleration levels within the vehicle, ensuring that personnel can comfortably endure extended durations inside. BAE Systems Hägglunds AB develops and produces an armored tracked vehicle called the CV90. Within the CV90’s suspension system, a key component known as the torsion bar, a rotational spring, plays a primary role in reducing the vehicle’s motion. The CV90 vehicle has seven wheels on each side, with each wheel having its dedicated torsion bar. To measure the whole-body vibration experienced within the vehicle, a measurement called the Vibrational Dose Value (VDV) is utilized. The main objective of this thesis is to develop a data-driven model to optimize the suspension system by identifying the combination of torsion bars that generates the smallest VDV. The data used for optimization is based on simulations of the CV90 vehicle in a virtual environment. In the simulation, the CV90 vehicle, with its full dynamics, is driven over a specific virtual road at a particular velocity. The simulation itself cannot be manipulated; only the input values can be adjusted. Thus, we consider the simulation as a black box, which led us to implement the black-box optimization algorithm known as Particle-Swarm. In this thesis, four different roads, each with velocities ranging from four to seven different levels, were provided to the optimization model. The results show that the model identifies a combination of torsion bars that generates a small VDV for all combinations of velocities and roads, with an average VDV improvement of around 20% - 60% compared to a reference case. Since this thesis serves as a proof of concept, the conclusion is that the devised method is effective and suitable for addressing the problem at hand. Nonetheless, for seamless integration of this method into the tracked vehicle development process, further research is necessary.

<p dir="ltr">Electric aircraft offer a promising avenue for reducing aviation's environmental impact through decreased greenhouse gas emissions and noise pollution. Nonetheless, their adoption is hindered by the challenge of limited operational range. Addressed in the study is the range limitation by integrating and optimizing multiple energy storage components—hydrogen fuel cells, Li-ion batteries, and ultracapacitors—through advanced energy management strategies. Utilizing meta-heuristic optimization methods, the research assessed the dynamic performance of each energy component and the effectiveness of the energy management strategy, primarily measured by the hydrogen consumption rate. MATLAB simulations validated the proposed approach, indicating a decrease in hydrogen usage, thus enhancing efficiency and potential cost savings. Artificial Gorilla Troop Optimization yielded the best results with the lowest average hydrogen consumption rate (102.62 grams), outperforming Particle Swarm Optimization (104.68 grams) and Ant Colony Optimization (105.96 grams). The findings suggested that employing a combined energy storage and optimization strategy can significantly improve the operational efficiency and energy conservation of electric aircraft. The study highlighted the potential of such strategies to extend the range of electric aircraft, contributing to a more sustainable aviation future.</p>

[ES] La búsqueda novedosa es un nuevo paradigma de los algoritmos de optimización, evolucionarios y bioinspirados, que está basado en la idea de forzar la búsqueda del óptimo global en aquellas partes inexploradas del dominio de la función que no son atractivas para el algoritmo, con la intención de evitar estancamientos en óptimos locales. La búsqueda novedosa se ha aplicado al algoritmo de optimización de enjambre de partículas, obteniendo un nuevo algoritmo denominado algoritmo de enjambre novedoso (NS). NS se ha aplicado al conjunto de pruebas sintéticas CEC2005, comparando los resultados con los obtenidos por otros algoritmos del estado del arte. Los resultados muestran un mejor comportamiento de NS en funciones altamente no lineales, a cambio de un aumento en la complejidad computacional. En lo que resta de trabajo, el algoritmo NS se ha aplicado en diferentes modelos, específicamente en el diseño de un motor de combustión interna, en la estimación de demanda de energía mediante gramáticas de enjambre, en la evolución del cáncer de vejiga de un paciente concreto y en la evolución del COVID-19. Cabe remarcar que, en el estudio de los modelos de COVID-19, se ha tenido en cuenta la incertidumbre, tanto de los datos como de la evolución de la enfermedad. / [CA] La cerca nova és un nou paradigma dels algoritmes d'optimització, evolucionaris i bioinspirats, que està basat en la idea de forçar la cerca de l'òptim global en les parts inexplorades del domini de la funció que no són atractives per a l'algoritme, amb la intenció d'evitar estancaments en òptims locals. La cerca nova s'ha aplicat a l'algoritme d'optimització d'eixam de partícules, obtenint un nou algoritme denominat algoritme d'eixam nou (NS). NS s'ha aplicat al conjunt de proves sintètiques CEC2005, comparant els resultats amb els obtinguts per altres algoritmes de l'estat de l'art. Els resultats mostren un millor comportament de NS en funcions altament no lineals, a canvi d'un augment en la complexitat computacional. En el que resta de treball, l'algoritme NS s'ha aplicat en diferents models, específicament en el disseny d'un motor de combustió interna, en l'estimació de demanda d'energia mitjançant gramàtiques d'eixam, en l'evolució del càncer de bufeta d'un pacient concret i en l'evolució del COVID-19. Cal remarcar que, en l'estudi dels models de COVID-19, s'ha tingut en compte la incertesa, tant de les dades com de l'evolució de la malaltia. / [EN] Novelty Search is a recent paradigm in evolutionary and bio-inspired optimization algorithms, based on the idea of forcing to look for those unexplored parts of the domain of the function that might be unattractive for the algorithm, with the aim of avoiding stagnation in local optima. Novelty Search has been applied to the Particle Swarm Optimization algorithm, obtaining a new algorithm named Novelty Swarm (NS). NS has been applied to the CEC2005 benchmark, comparing its results with other state of the art algorithms. The results show better behaviour in high nonlinear functions at the cost of increasing the computational complexity. During the rest of the thesis, the NS algorithm has been used in different models, specifically the design of an Internal Combustion Engine, the prediction of energy demand estimation with Grammatical Swarm, the evolution of the bladder cancer of a specific patient and the evolution of COVID-19. It is also remarkable that, in the study of COVID-19 models, uncertainty of the data and the evolution of the disease has been taken in account. / Martínez Rodríguez, D. (2021). Optimization Algorithm Based on Novelty Search Applied to the Treatment of Uncertainty in Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/178994

[pt] A previsão de séries temporais é fundamental na tomada de decisões de curto, médio e longo prazo, em diversas áreas como o setor elétrico, a bolsa de valores, a meteorologia, entre outros. Tem-se na atualidade uma diversidade de técnicas e modelos para realizar essas previsões, mas as ferramentas estatísticas são as mais utilizadas principalmente por apresentarem um maior grau de interpretabilidade. No entanto, as técnicas de inteligência computacional têm sido cada vez mais aplicadas em previsão de séries temporais, destacando-se as Redes Neurais Artificiais (RNA) e os Sistemas de Inferência Fuzzy (SIF). Recentemente foi criado um novo tipo de RNA, denominada Echo State Networks (ESN), as quais diferem das RNA clássicas por apresentarem uma camada escondida com conexões aleatórias, denominada de Reservoir (Reservatório). Este Reservoir é ativado pelas entradas da rede e pelos seus estados anteriores, gerando o efeito de Echo State (Eco), fornecendo assim um dinamismo e um desempenho melhor para tarefas de natureza temporal. Uma dificuldade dessas redes ESN é a presença de diversos parâmetros, tais como Raio Espectral, Tamanho do Reservoir e a Percentual de Conexão, que precisam ser calibrados para que a ESN forneça bons resultados. Portanto, este trabalho tem como principal objetivo o desenvolvimento de uma ferramenta computacional capaz de realizar previsões de séries temporais, baseada nas ESN, com ajuste automático de seus parâmetros por Particle Swarm Optimization (PSO) e Algoritmos Genéticos (GA), facilitando a sua utilização pelo usuário. A ferramenta computacional desenvolvida oferece uma interface gráfica intuitiva e amigável, tanto em termos da modelagem da ESN, quanto em termos de realização de eventuais pré-processamentos na série a ser prevista. / [en] The time series forecasting is critical to decision making in the short, medium and long term in several areas such as electrical, stock market, weather and industry. Today exist different techniques to model this forecast, but statistics are more used, because they have a bigger interpretability, due by the mathematic models created. However, intelligent techniques are being more applied in time series forecasting, where the principal models are the Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS). A new type of ANN called Echo State Networks (ESN) was created recently, which differs from the classic ANN in a randomly connected hidden layer called Reservoir. This Reservoir is activated by the network inputs, and the historic of the reservoir activations generating so, the Echo State and giving to the network more dynamism and a better performance in temporal nature tasks. One problem with these networks is the presence of some parameters as, Spectral Radius, Reservoir Size and Connection Percent, which require calibration to make the network provide positive results. Therefore the aim of this work is to develop a computational application capable to do time series forecasting, based on ESN, with automatic parameters adjustment by Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), facilitating its use by the user. The developed computational tool offers an intuitive and friendly interface, both in terms of modeling the ESN, and in terms of achievement of possible pre-process on the series to be forecasted.

The purpose of this thesis is to present solutions to overcome several key difficulties that limit the application of numerical modelling in communication cable design and analysis. In particular, specific limiting factors are that simulations are time consuming, and the process of comparison requires skill and is poorly defined and understood. When much of the process of design consists of optimisation of performance within a well defined domain, the use of artificial intelligence techniques may reduce or remove the need for human interaction in the design process. The automation of human processes allows round-the-clock operation at a faster throughput. Achieving a speedup would permit greater exploration of the possible designs, improving understanding of the domain. This thesis presents work that relates to three facets of the efficiency of numerical modelling: minimizing simulation execution time, controlling optimization processes and quantifying comparisons of results. These topics are of interest because simulation times for most problems of interest run into tens of hours. The design process for most systems being modelled may be considered an optimisation process in so far as the design is improved based upon a comparison of the test results with a specification. Development of software to automate this process permits the improvements to continue outside working hours, and produces decisions unaffected by the psychological state of a human operator. Improved performance of simulation tools would facilitate exploration of more variations on a design, which would improve understanding of the problem domain, promoting a virtuous circle of design. The minimization of execution time was achieved through the development of a Parallel TLM Solver which did not use specialized hardware or a dedicated network. Its design was novel because it was intended to operate on a network of heterogeneous machines in a manner which was fault tolerant, and included a means to reduce vulnerability of simulated data without encryption. Optimisation processes were controlled by genetic algorithms and particle swarm optimisation which were novel applications in communication cable design. The work extended the range of cable parameters, reducing conductor diameters for twisted pair cables, and reducing optical coverage of screens for a given shielding effectiveness. Work on the comparison of results introduced ―Colour maps‖ as a way of displaying three scalar variables over a two-dimensional surface, and comparisons were quantified by extending 1D Feature Selective Validation (FSV) to two dimensions, using an ellipse shaped filter, in such a way that it could be extended to higher dimensions. In so doing, some problems with FSV were detected, and suggestions for overcoming these presented: such as the special case of zero valued DC signals. A re-description of Feature Selective Validation, using Jacobians and tensors is proposed, in order to facilitate its implementation in higher dimensional spaces.

Η παρούσα διατριβή ασχολείται με τη μελέτη, την ανάπτυξη και τη θεμελίωση νέων μεθόδων Υπολογιστικής Νοημοσύνης και Ευφυούς Βελτιστοποίησης. Συνοπτικά οργανώνεται στα ακόλουθα τρία μέρη: Αρχικά παρουσιάζεται το πεδίο της Υπολογιστικής Νοημοσύνης και πραγματοποιείται μία σύντομη αναφορά στους τρεις κύριους κλάδους της, τον Εξελικτικό Υπολογισμό, τα Τεχνητά Νευρωνικά Δίκτυα και τα Ασαφή Συστήματα. Το επόμενο μέρος αφιερώνεται στην παρουσίαση νέων, καινοτόμων οικογενειών των αλγορίθμων Βελτιστοποίησης Σμήνους Σωματιδίων (ΒΣΣ) και των Διαφοροεξελικτικών Αλγόριθμων (ΔΕΑ), για την επίλυση αριθμητικών προβλημάτων βελτιστοποίησης χωρίς περιορισμούς, έχοντας είτε ένα, είτε πολλαπλούς ολικούς βελτιστοποιητές. Οι αλγόριθμοι ΒΣΣ και ΔΕΑ αποτελούν τις βασικές μεθοδολογίες της παρούσας διατριβής. Όλες οι οικογένειες μεθόδων που προτείνονται, βασίζονται σε παρατηρήσεις των κοινών δομικών χαρακτηριστικών των ΒΣΣ και ΔΕΑ, ενώ η κάθε προτεινόμενη οικογένεια τις αξιοποιεί με διαφορετικό τρόπο, δημιουργώντας νέες, αποδοτικές μεθόδους με αρκετά ενδιαφέρουσες ιδιότητες και δυναμική. Η παρουσίαση του ερευνητικού έργου της διατριβής ολοκληρώνεται με το τρίτο μέρος στο οποίο περιλαμβάνεται μελέτη και ανάπτυξη μεθόδων ολικής βελτιστοποίησης για την εκπαίδευση Τεχνητών Νευρωνικών Δικτύων Υψηλής Τάξης, σε σειριακά και παράλληλα ή / και κατανεμημένα υπολογιστικά συστήματα. Η διδακτορική διατριβή ολοκληρώνεται με βασικά συμπεράσματα και τη συνεισφορά της. / The main subject of the thesis at hand revolves mainly around the development and foundations of new methods of computational intelligence and intelligent optimization. The thesis is organized into the following three parts: Firstly, we briefly present an overview of the field of Computational Intelligence, by describing its main categories, the Evolutionary Computation, the Artificial Neural Networks and the Fuzzy Systems. In the second part, we provide a detailed description of the newly developed families of algorithms for solving unconstrained numerical optimization problems in continues spaces with at least one global optimum. The proposed families are based on two well-known and widely used algorithms, namely the Particle Swarm Optimization (PSO) and the Differential Evolution (DE) algorithm. Both DE and PSO are the basic components for almost all methodologies proposed in the thesis. The proposed methodologies are based on common observations of the dynamics, the structural and the spacial characteristics of DE and PSO algorithms. Four novel families are presented in this part which exploit the aforementioned characteristics of the DE and the PSO algorithms. The proposed methodologies are efficient methods with quite interesting properties and dynamics. The presentation and description of our research contribution ends with the third and last part of the thesis, which includes the study and the development of novel global optimization methodologies for training Higher order Artificial Neural Networks in serial and parallel / distributed computational environments. The thesis ends with a brief summary, conclusions and discussion of the contribution of this thesis.

Cette thèse a pour objectif principal le développement de nouveaux algorithmes globaux pour la résolution de problèmes d’optimisation mono et multi-objectif, en se basant sur des formules de représentation ayant la tâche principale de générer des points initiaux appartenant à une zone proche du minimum globale. Dans ce contexte, une nouvelle approche appelée RFNM est proposée et testée sur plusieurs fonctions non linéaires, non différentiables et multimodales. D’autre part, une extension à la dimension infinie a été établie en proposant une démarche pour la recherche du minimum global. Par ailleurs, plusieurs problèmes de conception mécanique, à caractère aléatoire, ont été considérés et résolus en utilisant cette approche, avec amélioration de la méthode multi-objectif NNC. Enfin, une contribution à l'optimisation multi-objectif par une nouvelle approche a été proposée. Elle permet de générer un nombre suffisant de points pour représenter la solution optimale de Pareto. / The main objective of this work is to develop new global algorithms to solve single and multi-objective optimization problems, based on the representation formulas with the main task to generate initial points belonging to an area close to the global minimum. In this context, a new approach called RFNM is proposed and tested on several nonlinear, non-differentiable and multimodal finctions. On the other hand, an extension to the infinite dimension was established by proposing an approach for finding the global minimum. Moreover,several random mechanical design problems were considered and resolved using this approach, and improving the NNC multi-objective method. Finally, a new multi-objective optimization method called RSMO is presented. It solves the multi-objective optimization problems by generating a sufficient number o fpoints in the Pareto front.

L'objet de cette thèse est de proposer un cadre complet, du haut niveau au bas niveau, de génération de trajectoires pour un groupe de systèmes dynamiques indépendants. Ce cadre, basé sur la résolution de l'équation de Burgers pour la génération de trajectoires, est appliqué à un modèle original de drone trirotor et utilise la platitude des deux systèmes différentiels considérés. La première partie du manuscrit est consacrée à la génération de trajectoires. Celle-ci est effectuée en créant formellement, par le biais de la platitude du système considéré, des solutions à l'équation de la chaleur. Ces solutions sont transformées en solution de l'équation de Burgers par la transformation de Hopf-Cole pour correspondre aux formations voulues. Elles sont optimisées pour répondre à des contraintes spécifiques. Plusieurs exemples de trajectoires sont donnés.La deuxième partie est consacrée au suivi autonome de trajectoire par un drone trirotor. Ce drone est totalement actionné et un contrôleur en boucle fermée non-linéaire est proposé. Celui-ci est testé en suivant, en roulant, des trajectoires au sol et en vol. Un modèle est présenté et une démarche pour le contrôle est proposée pour transporter une charge pendulaire. / This thesis is dedicated to the creation of a complete framework, from high-level to low-level, of trajectory generation for a group of independent dynamical systems. This framework, based for the trajectory generation, on the resolution of Burgers equation, is applied to a novel model of trirotor UAV and uses the flatness of the two levels of dynamical systems.The first part of this thesis is dedicated to the generation of trajectories. Formal solutions to the heat equation are created using the differential flatness of this equation. These solutions are transformed into solutions to Burgers' equation through Hopf-Cole transformation to match the desired formations. They are optimized to match specific requirements. Several examples of trajectories are given.The second part is dedicated to the autonomous trajectory tracking by a trirotor UAV. This UAV is totally actuated and a nonlinear closed-loop controller is suggested. This controller is tested on the ground and in flight by tracking, rolling or flying, a trajectory. A model is presented and a control approach is suggested to transport a pendulum load.

Durante la última década, los métodos de optimización heurísticos basados en imitar a nivel computacional procesos naturales, biológicos, sociales o culturales, han despertado el interés de la comunidad científica debido a su capacidad para explorar eficientemente espacios de soluciones multimodales y multidimensionales. En este ámbito, esta tesis aborda el desarrollo, análisis y puesta a punto de diferentes métodos de optimización tradicionales y heurísticos. En concreto, se considera un método de búsqueda local basado en símplex y varios métodos heurísticos, tales como el recocido simulado, los algoritmos genéticos y la optimización con enjambre de partículas. Para estos dos últimos algoritmos se investigan diferentes esquemas con el objetivo de superar las limitaciones propias de los esquemas clásicos.La puesta a punto de los diferentes métodos de optimización se realiza considerando como problema de referencia la caracterización de la radiación de antenas a partir de medidas en campo próximo sobre geometría plana, utilizando un método de transformación de campo cercano a campo lejano basado en corrientes equivalentes. Para cada método de optimización se incluye un análisis paramétrico, en los casos en los que se ha considerado necesario, así como resultados de transformación de campo teóricos obtenidos para diferentes antenas de apertura y antenas de bocina piramidal. Los resultados de un estudio comparativo, realizado utilizando fuentes teóricas y medidas, demuestran la utilidad del método y permiten concluir que la optimización con enjambre de partículas es el algoritmo que proporciona las mejores prestaciones para esta aplicación.Los métodos de optimización desarrollados e investigados en este trabajo han sido también aplicados a otros problemas como son la síntesis de agrupaciones lineales o el modelado de fuente en aplicaciones de compatibilidad electromagnética. / For the last decade, heuristic optimization methods based on imitating natural, biological, social or cultural processes in a computational way have aroused great interest among the scientific community, due to its ability to explore efficiently multimodal and high-dimension solution spaces. On this basis, this thesis tackles the development, analysis and tuning of different traditional and heuristic optimization methods. In short, a local search method based on simplex and several heuristic methods, such as simulated annealing, genetic algorithms and particle swarm optimization are considered. For these last two algorithms different schemes are investigated so as to overcome the typical limitations of classical schemes.The tuning of the optimization methods is carried out considering as a reference problem the antenna radiation characterization from near-field measurements over a planar geometry, using a near-field to far-field transformation method based on equivalent currents. A parametric analysis is included for each optimization method, in those cases in which it has been considered necessary, as well as theoretical field transformation results obtained with aperture and pyramidal horn antennas. Results of a comparative study, carried out using theoretical sources and measurements, demonstrate the usefulness of the method and make it possible to conclude that the particle swarm optimization is the algorithm that provides the best performance for this application.The optimization methods developed and investigated in this work have also been applied to other problems, such as the synthesis of linear arrays or the source modelling in electromagnetic compatibility applications.