Multi-objective optimization of material model parameters of an adhesive layer by using SPEA2

The usage of multi material structures in industry, especially in the automotive industry are increasing. To overcome the difficulties in joining these structures, adhesives have several benefits over traditional joining methods. Therefore, accurate simulations of the entire process of fracture including the adhesive layer is crucial. In this paper, material parameters of a previously developed meso mechanical finite element (FE) model of a thin adhesive layer are optimized using the Strength Pareto Evolutionary Algorithm (SPEA2). Objective functions are defined as the error between experimental data and simulation data. The experimental data is provided by previously performed experiments where an adhesive layer was loaded in monotonically increasing peel and shear. Two objective functions are dependent on 9 model parameters (decision variables) in total and are evaluated by running two FEsimulations, one is loading the adhesive layer in peel and the other in shear. The original study converted the two objective functions into one function that resulted in one optimal solution. In this study, however, a Pareto frontis obtained by employing the SPEA2 algorithm. Thus, more insight into the material model, objective functions, optimal solutions and decision space is acquired using the Pareto front. We compare the results and show good agreement with the experimental data.

Design and Comparison оf Mixed Hinf/H2 Controller fоr АMB System

This master’s thesis is focused on the active magnetic bearings control, specifically the robust control. As carrying out of such kind of control used mixed H2/Hinf controller. So the goal of this work is to design it using Robust Control Toolbox™ in MATLAB and compare it performance and robustness with Hinf robust controller characteristics. But only one degree-of-freedom controller considered.

Double-objective economic statistical design of the VP T-2 control chart: Wald's identity approach

Research has shown that applying the T-2 control chart by using a variable parameters (VP) scheme yields rapid detection of out-of-control states. In this paper, the problem of economic statistical design of the VP T-2 control chart is considered as a double-objective minimization problem with the statistical objective being the adjusted average time to signal and the economic objective being expected cost per hour. We then find the Pareto-optimal designs in which the two objectives are met simultaneously by using a multi-objective genetic algorithm. Through an illustrative example, we show that relatively large benefits can be achieved by applying the VP scheme when compared with usual schemes, and in addition, the multi-objective approach provides the user with designs that are flexible and adaptive.

Design and control of multi-finger haptic devices for dexterous manipulation

En la interacción con el entorno que nos rodea durante nuestra vida diaria (utilizar un cepillo de dientes, abrir puertas, utilizar el teléfono móvil, etc.) y en situaciones profesionales (intervenciones médicas, procesos de producción, etc.), típicamente realizamos manipulaciones avanzadas que incluyen la utilización de los dedos de ambas manos. De esta forma el desarrollo de métodos de interacción háptica multi-dedo dan lugar a interfaces hombre-máquina más naturales y realistas. No obstante, la mayoría de interfaces hápticas disponibles en el mercado están basadas en interacciones con un solo punto de contacto; esto puede ser suficiente para la exploración o palpación del entorno pero no permite la realización de tareas más avanzadas como agarres. En esta tesis, se investiga el diseño mecánico, control y aplicaciones de dispositivos hápticos modulares con capacidad de reflexión de fuerzas en los dedos índice, corazón y pulgar del usuario. El diseño mecánico de la interfaz diseñada, ha sido optimizado con funciones multi-objetivo para conseguir una baja inercia, un amplio espacio de trabajo, alta manipulabilidad y reflexión de fuerzas superiores a 3 N en el espacio de trabajo. El ancho de banda y la rigidez del dispositivo se han evaluado mediante simulación y experimentación real. Una de las áreas más importantes en el diseño de estos dispositivos es el efector final, ya que es la parte que está en contacto con el usuario. Durante este trabajo se ha diseñado un dedal de bajo peso, adaptable a diferentes usuarios que, mediante la incorporación de sensores de contacto, permite estimar fuerzas normales y tangenciales durante la interacción con entornos reales y virtuales. Para el diseño de la arquitectura de control, se estudiaron los principales requisitos para estos dispositivos. Entre estos, cabe destacar la adquisición, procesado e intercambio a través de internet de numerosas señales de control e instrumentación; la computación de equaciones matemáticas incluyendo la cinemática directa e inversa, jacobiana, algoritmos de detección de agarres, etc. Todos estos componentes deben calcularse en tiempo real garantizando una frecuencia mínima de 1 KHz. Además, se describen sistemas para manipulación de precisión virtual y remota; así como el diseño de un método denominado "desacoplo cinemático iterativo" para computar la cinemática inversa de robots y la comparación con otros métodos actuales. Para entender la importancia de la interacción multimodal, se ha llevado a cabo un estudio para comprobar qué estímulos sensoriales se correlacionan con tiempos de respuesta más rápidos y de mayor precisión. Estos experimentos se desarrollaron en colaboración con neurocientíficos del instituto Technion Israel Institute of Technology. Comparando los tiempos de respuesta en la interacción unimodal (auditiva, visual y háptica) con combinaciones bimodales y trimodales de los mismos, se demuestra que el movimiento sincronizado de los dedos para generar respuestas de agarre se basa principalmente en la percepción háptica. La ventaja en el tiempo de procesamiento de los estímulos hápticos, sugiere que los entornos virtuales que incluyen esta componente sensorial generan mejores contingencias motoras y mejoran la credibilidad de los eventos. Se concluye que, los sistemas que incluyen percepción háptica dotan a los usuarios de más tiempo en las etapas cognitivas para rellenar información de forma creativa y formar una experiencia más rica. Una aplicación interesante de los dispositivos hápticos es el diseño de nuevos simuladores que permitan entrenar habilidades manuales en el sector médico. En colaboración con fisioterapeutas de Griffith University en Australia, se desarrolló un simulador que permite realizar ejercicios de rehabilitación de la mano. Las propiedades de rigidez no lineales de la articulación metacarpofalange del dedo índice se estimaron mediante la utilización del efector final diseñado. Estos parámetros, se han implementado en un escenario que simula el comportamiento de la mano humana y que permite la interacción háptica a través de esta interfaz. Las aplicaciones potenciales de este simulador están relacionadas con entrenamiento y educación de estudiantes de fisioterapia. En esta tesis, se han desarrollado nuevos métodos que permiten el control simultáneo de robots y manos robóticas en la interacción con entornos reales. El espacio de trabajo alcanzable por el dispositivo háptico, se extiende mediante el cambio de modo de control automático entre posición y velocidad. Además, estos métodos permiten reconocer el gesto del usuario durante las primeras etapas de aproximación al objeto para su agarre. Mediante experimentos de manipulación avanzada de objetos con un manipulador y diferentes manos robóticas, se muestra que el tiempo en realizar una tarea se reduce y que el sistema permite la realización de la tarea con precisión. Este trabajo, es el resultado de una colaboración con investigadores de Harvard BioRobotics Laboratory. ABSTRACT When we interact with the environment in our daily life (using a toothbrush, opening doors, using cell-phones, etc.), or in professional situations (medical interventions, manufacturing processes, etc.) we typically perform dexterous manipulations that involve multiple fingers and palm for both hands. Therefore, multi-Finger haptic methods can provide a realistic and natural human-machine interface to enhance immersion when interacting with simulated or remote environments. Most commercial devices allow haptic interaction with only one contact point, which may be sufficient for some exploration or palpation tasks but are not enough to perform advanced object manipulations such as grasping. In this thesis, I investigate the mechanical design, control and applications of a modular haptic device that can provide force feedback to the index, thumb and middle fingers of the user. The designed mechanical device is optimized with a multi-objective design function to achieve a low inertia, a large workspace, manipulability, and force-feedback of up to 3 N within the workspace; the bandwidth and rigidity for the device is assessed through simulation and real experimentation. One of the most important areas when designing haptic devices is the end-effector, since it is in contact with the user. In this thesis the design and evaluation of a thimble-like, lightweight, user-adaptable, and cost-effective device that incorporates four contact force sensors is described. This design allows estimation of the forces applied by a user during manipulation of virtual and real objects. The design of a real-time, modular control architecture for multi-finger haptic interaction is described. Requirements for control of multi-finger haptic devices are explored. Moreover, a large number of signals have to be acquired, processed, sent over the network and mathematical computations such as device direct and inverse kinematics, jacobian, grasp detection algorithms, etc. have to be calculated in Real Time to assure the required high fidelity for the haptic interaction. The Hardware control architecture has different modules and consists of an FPGA for the low-level controller and a RT controller for managing all the complex calculations (jacobian, kinematics, etc.); this provides a compact and scalable solution for the required high computation capabilities assuring a correct frequency rate for the control loop of 1 kHz. A set-up for dexterous virtual and real manipulation is described. Moreover, a new algorithm named the iterative kinematic decoupling method was implemented to solve the inverse kinematics of a robotic manipulator. In order to understand the importance of multi-modal interaction including haptics, a subject study was carried out to look for sensory stimuli that correlate with fast response time and enhanced accuracy. This experiment was carried out in collaboration with neuro-scientists from Technion Israel Institute of Technology. By comparing the grasping response times in unimodal (auditory, visual, and haptic) events with the response times in events with bimodal and trimodal combinations. It is concluded that in grasping tasks the synchronized motion of the fingers to generate the grasping response relies on haptic cues. This processing-speed advantage of haptic cues suggests that multimodalhaptic virtual environments are superior in generating motor contingencies, enhancing the plausibility of events. Applications that include haptics provide users with more time at the cognitive stages to fill in missing information creatively and form a richer experience. A major application of haptic devices is the design of new simulators to train manual skills for the medical sector. In collaboration with physical therapists from Griffith University in Australia, we developed a simulator to allow hand rehabilitation manipulations. First, the non-linear stiffness properties of the metacarpophalangeal joint of the index finger were estimated by using the designed end-effector; these parameters are implemented in a scenario that simulates the behavior of the human hand and that allows haptic interaction through the designed haptic device. The potential application of this work is related to educational and medical training purposes. In this thesis, new methods to simultaneously control the position and orientation of a robotic manipulator and the grasp of a robotic hand when interacting with large real environments are studied. The reachable workspace is extended by automatically switching between rate and position control modes. Moreover, the human hand gesture is recognized by reading the relative movements of the index, thumb and middle fingers of the user during the early stages of the approximation-to-the-object phase and then mapped to the robotic hand actuators. These methods are validated to perform dexterous manipulation of objects with a robotic manipulator, and different robotic hands. This work is the result of a research collaboration with researchers from the Harvard BioRobotics Laboratory. The developed experiments show that the overall task time is reduced and that the developed methods allow for full dexterity and correct completion of dexterous manipulations.

Fast experimental identification of suspension models for control design

This paper presents both the theoretical and the experimental approaches of the development of a mathematical model to be used in multi-variable control system designs of an active suspension for a sport utility vehicle (SUV), in this case a light pickup truck. A complete seven-degree-of-freedom model is successfully quickly identified, with very satisfactory results in simulations and in real experiments conducted with the pickup truth. The novelty of the proposed methodology is the use of commercial software in the early stages of the identification to speed up the process and to minimize the need for a large number of costly experiments. The paper also presents major contributions to the identification of uncertainties in vehicle suspension models and in the development of identification methods using the sequential quadratic programming, where an innovation regarding the calculation of the objective function is proposed and implemented. Results from simulations of and practical experiments with the real SUV are presented, analysed, and compared, showing the potential of the method.

Multi-criteria optimization manipulator trajectory planning

In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling, image processing, and pattern and speech recognition (Bäck et al., 1997). In robotics the problems of trajectory planning, collision avoidance and manipulator structure design considering a single criteria has been solved using several techniques (Alander, 2003). Most engineering applications require the optimization of several criteria simultaneously. Often the problems are complex, include discrete and continuous variables and there is no prior knowledge about the search space. These kind of problems are very more complex, since they consider multiple design criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective) optimization, that has been addressed successfully through GAs (Deb, 2001). The overall aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible and iii) to achieve a Pareto front uniformly spread. Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area. In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA) (Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello, 1998). In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ), ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance, end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint and end effector traveled distance, trajectory ripple and energy required by the manipulator to reach at destination point. Bearing this ideas in mind, the paper addresses the planning of robot trajectories, meaning the development of an algorithm to find a continuous motion that takes the manipulator from a given starting configuration up to a desired end position without colliding with any obstacle in the workspace. The chapter is organized as follows. Section 2 describes the trajectory planning and several approaches proposed in the literature. Section 3 formulates the problem, namely the representation adopted to solve the trajectory planning and the objectives considered in the optimization. Section 4 studies the algorithm convergence. Section 5 studies a 2R manipulator (i.e., a robot with two rotational joints/links) when the optimization trajectory considers two and five objectives. Sections 6 and 7 show the results for the 3R redundant manipulator with five goals and for other complementary experiments are described, respectively. Finally, section 8 draws the main conclusions.

Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for large-scale systems. Nonetheless, a critical obstacle, which needs to be overcome in MPC, is the large computational burden when a large-scale system is considered or a long prediction horizon is involved. In order to solve this problem, we use an adaptive prediction accuracy (APA) approach that can reduce the computational burden almost by half. The proposed MPC scheme with this scheme is tested on the northern Dutch water system, which comprises Lake IJssel, Lake Marker, the River IJssel and the North Sea Canal. The simulation results show that by using the MPC-APA scheme, the computational time can be reduced to a large extent and a flood protection problem over longer prediction horizons can be well solved.

Optimal game theoretic distributed control methodologies in small scale power systems

This dissertation presents the competitive control methodologies for small-scale power system (SSPS). A SSPS is a collection of sources and loads that shares a common network which can be isolated during terrestrial disturbances. Micro-grids, naval ship electric power systems (NSEPS), aircraft power systems and telecommunication system power systems are typical examples of SSPS. The analysis and development of control systems for small-scale power systems (SSPS) lacks a defined slack bus. In addition, a change of a load or source will influence the real time system parameters of the system. Therefore, the control system should provide the required flexibility, to ensure operation as a single aggregated system. In most of the cases of a SSPS the sources and loads must be equipped with power electronic interfaces which can be modeled as a dynamic controllable quantity. The mathematical formulation of the micro-grid is carried out with the help of game theory, optimal control and fundamental theory of electrical power systems. Then the micro-grid can be viewed as a dynamical multi-objective optimization problem with nonlinear objectives and variables. Basically detailed analysis was done with optimal solutions with regards to start up transient modeling, bus selection modeling and level of communication within the micro-grids. In each approach a detail mathematical model is formed to observe the system response. The differential game theoretic approach was also used for modeling and optimization of startup transients. The startup transient controller was implemented with open loop, PI and feedback control methodologies. Then the hardware implementation was carried out to validate the theoretical results. The proposed game theoretic controller shows higher performances over traditional the PI controller during startup. In addition, the optimal transient surface is necessary while implementing the feedback controller for startup transient. Further, the experimental results are in agreement with the theoretical simulation. The bus selection and team communication was modeled with discrete and continuous game theory models. Although players have multiple choices, this controller is capable of choosing the optimum bus. Next the team communication structures are able to optimize the players’ Nash equilibrium point. All mathematical models are based on the local information of the load or source. As a result, these models are the keys to developing accurate distributed controllers.

Inteligencia artificial para la predicción y control del acabado superficial en procesos de fresado a alta velocidad

El objetivo principal de esta tesis doctoral es profundizar en el análisis y diseño de un sistema inteligente para la predicción y control del acabado superficial en un proceso de fresado a alta velocidad, basado fundamentalmente en clasificadores Bayesianos, con el prop´osito de desarrollar una metodolog´ıa que facilite el diseño de este tipo de sistemas. El sistema, cuyo propósito es posibilitar la predicción y control de la rugosidad superficial, se compone de un modelo aprendido a partir de datos experimentales con redes Bayesianas, que ayudar´a a comprender los procesos dinámicos involucrados en el mecanizado y las interacciones entre las variables relevantes. Dado que las redes neuronales artificiales son modelos ampliamente utilizados en procesos de corte de materiales, también se incluye un modelo para fresado usándolas, donde se introdujo la geometría y la dureza del material como variables novedosas hasta ahora no estudiadas en este contexto. Por lo tanto, una importante contribución en esta tesis son estos dos modelos para la predicción de la rugosidad superficial, que se comparan con respecto a diferentes aspectos: la influencia de las nuevas variables, los indicadores de evaluación del desempeño, interpretabilidad. Uno de los principales problemas en la modelización con clasificadores Bayesianos es la comprensión de las enormes tablas de probabilidad a posteriori producidas. Introducimos un m´etodo de explicación que genera un conjunto de reglas obtenidas de árboles de decisión. Estos árboles son inducidos a partir de un conjunto de datos simulados generados de las probabilidades a posteriori de la variable clase, calculadas con la red Bayesiana aprendida a partir de un conjunto de datos de entrenamiento. Por último, contribuimos en el campo multiobjetivo en el caso de que algunos de los objetivos no se puedan cuantificar en números reales, sino como funciones en intervalo de valores. Esto ocurre a menudo en aplicaciones de aprendizaje automático, especialmente las basadas en clasificación supervisada. En concreto, se extienden las ideas de dominancia y frontera de Pareto a esta situación. Su aplicación a los estudios de predicción de la rugosidad superficial en el caso de maximizar al mismo tiempo la sensibilidad y la especificidad del clasificador inducido de la red Bayesiana, y no solo maximizar la tasa de clasificación correcta. Los intervalos de estos dos objetivos provienen de un m´etodo de estimación honesta de ambos objetivos, como e.g. validación cruzada en k rodajas o bootstrap.---ABSTRACT---The main objective of this PhD Thesis is to go more deeply into the analysis and design of an intelligent system for surface roughness prediction and control in the end-milling machining process, based fundamentally on Bayesian network classifiers, with the aim of developing a methodology that makes easier the design of this type of systems. The system, whose purpose is to make possible the surface roughness prediction and control, consists of a model learnt from experimental data with the aid of Bayesian networks, that will help to understand the dynamic processes involved in the machining and the interactions among the relevant variables. Since artificial neural networks are models widely used in material cutting proceses, we include also an end-milling model using them, where the geometry and hardness of the piecework are introduced as novel variables not studied so far within this context. Thus, an important contribution in this thesis is these two models for surface roughness prediction, that are then compared with respecto to different aspects: influence of the new variables, performance evaluation metrics, interpretability. One of the main problems with Bayesian classifier-based modelling is the understanding of the enormous posterior probabilitiy tables produced. We introduce an explanation method that generates a set of rules obtained from decision trees. Such trees are induced from a simulated data set generated from the posterior probabilities of the class variable, calculated with the Bayesian network learned from a training data set. Finally, we contribute in the multi-objective field in the case that some of the objectives cannot be quantified as real numbers but as interval-valued functions. This often occurs in machine learning applications, especially those based on supervised classification. Specifically, the dominance and Pareto front ideas are extended to this setting. Its application to the surface roughness prediction studies the case of maximizing simultaneously the sensitivity and specificity of the induced Bayesian network classifier, rather than only maximizing the correct classification rate. Intervals in these two objectives come from a honest estimation method of both objectives, like e.g. k-fold cross-validation or bootstrap.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Design methodology for multi-pumped discrete Raman amplifiers: case-study employing photonic crystal fibers

This paper proposes a new design methodology for discrete multi-pumped Raman amplifier. In a multi-objective optimization scenario, in a first step the whole solution-space is inspected by a CW analytical formulation. Then, the most promising solutions are fully investigated by a rigorous numerical treatment and the Raman amplification performance is thus determined by the combination of analytical and numerical approaches. As an application of our methodology we designed an photonic crystal fiber Raman amplifier configuration which provides low ripple, high gain, clear eye opening and a low power penalty. The amplifier configuration also enables to fully compensate the dispersion introduced by a 70-km singlemode fiber in a 10 Gbit/s system. We have successfully obtained a configuration with 8.5 dB average gain over the C-band and 0.71 dB ripple with almost zero eye-penalty using only two pump lasers with relatively low pump power. (C) 2009 Optical Society of America

A hybrid planning method for transmission networks in a deregulated environment

The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open access schemes. The method starts with a candidate pool of feasible expansion plans. Consequent selection of the best candidates is carried out through a MOOP approach, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in context of deregulated system. Human knowledge has been applied in both stages to ensure the selection with practical engineering and management concerns. The expansion plan from MOOP is assessed by reliability criteria before it is finalized. The proposed method has been tested with the IEEE 14-bus system and relevant analyses and discussions have been presented.

Gestão de Recursos Energéticos nas SmartGrids

A sustentabilidade do sistema energético é crucial para o desenvolvimento económico e social das sociedades presentes e futuras. Para garantir o bom funcionamento dos sistemas de energia actua-se, tipicamente, sobre a produção e sobre as redes de transporte e de distribuição. No entanto, a integração crescente de produção distribuída, principalmente nas redes de distribuição de média e de baixa tensão, a liberalização dos mercados energéticos, o desenvolvimento de mecanismos de armazenamento de energia, o desenvolvimento de sistemas automatizados de controlo de cargas e os avanços tecnológicos das infra-estruturas de comunicação impõem o desenvolvimento de novos métodos de gestão e controlo dos sistemas de energia. O contributo deste trabalho é o desenvolvimento de uma metodologia de gestão de recursos energéticos num contexto de SmartGrids, considerando uma entidade designada por VPP que gere um conjunto de instalações (unidades produtoras, consumidores e unidades de armazenamento) e, em alguns casos, tem ao seu cuidado a gestão de uma parte da rede eléctrica. Os métodos desenvolvidos contemplam a penetração intensiva de produção distribuída, o aparecimento de programas de Demand Response e o desenvolvimento de novos sistemas de armazenamento. São ainda propostos níveis de controlo e de tomada de decisão hierarquizados e geridos por entidades que actuem num ambiente de cooperação mas também de concorrência entre si. A metodologia proposta foi desenvolvida recorrendo a técnicas determinísticas, nomeadamente, à programação não linear inteira mista, tendo sido consideradas três funções objectivo distintas (custos mínimos, emissões mínimas e cortes de carga mínimos), originando, posteriormente, uma função objectivo global, o que permitiu determinar os óptimos de Pareto. São ainda determinados os valores dos custos marginais locais em cada barramento e consideradas as incertezas dos dados de entrada, nomeadamente, produção e consumo. Assim, o VPP tem ao seu dispor um conjunto de soluções que lhe permitirão tomar decisões mais fundamentadas e de acordo com o seu perfil de actuação. São apresentados dois casos de estudo. O primeiro utiliza uma rede de distribuição de 32 barramentos publicada por Baran & Wu. O segundo caso de estudo utiliza uma rede de distribuição de 114 barramentos adaptada da rede de 123 barramentos do IEEE.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.