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Car following (CF) behaviour modelling has made significant progress in both traffic engi-neering and traffic psychology during recent decades. Autonomous vehicles (AVs) have been demonstrated to optimise traffic flow and increase traffic stability. Consequently, sever-al car-following models have been proposed based on various car following criteria, leading to a range of model parameter sets. In traffic engineering, Intelligent Driving Model (IDM) are commonly used as microscopic traffic flow models to simulate a single vehicle's behav-iour on a road. Observational data can be employed to parameter calibrate IDM models, which enhances their practicality for real-world applications. As a result, the calibration of model parameters is crucial in traffic simulation research and typically involves solving an optimization problem. Within the given context, the Nelder-Mead（NM）algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are utilized in this study for parameterizing the IDM model, using abundant trajectory data from five different road conditions. The study further examines the effects of various algorithms on the IDM model in different road sections, providing useful insights for traffic simulation and optimization.:Table of Contents CHAPTER 1 INTRODUCTION 1 1.1 BACKGROUND AND MOTIVATION 1 1.2 STRUCTURE OF THE WORK 3 CHAPTER 2 BACKGROUND AND RELATED WORK 4 2.1 CAR-FOLLOWING MODELS 4 2.1.1 General Motors model and Gazis-Herman-Rothery model 5 2.1.2 Optimal velocity model and extended models 6 2.1.3 Safety distance or collision avoidance models 7 2.1.4 Physiology-psychology models 8 2.1.5 Intelligent Driver model 10 2.2 CALIBRATION OF CAR-FOLLOWING MODEL 12 2.2.1 Statistical Methods 13 2.2.2 Optimization Algorithms 14 2.3 TRAJECTORY DATA 21 2.3.1 Requirements of Experimental Data 22 2.3.2 Data Collection Techniques 22 2.3.3 Collected Experimental Data 24 CHAPTER 3 EXPERIMENTS AND RESULTS 28 3.1 CALIBRATION PROCESS 28 3.1.1 Objective Function 29 3.1.2 Errors Analysis 30 3.2 SOFTWARE AND METHODOLOGY 30 3.3 NM RESULTS 30 3.4 PSO RESULTS 37 3.4.1 PSO Calibrator 37 3.4.2 PSO Results 44 3.5 GA RESULTS 51 3.6 OPTIMIZATION PERFORMANCE ANALYSIS 58 CHAPTER 4 CONCLUSION 60 REFERENCES 62

[pt] Com o objetivo de desenvolver um sistema de apoio à decisão na área de manutenção embarcada, este trabalho apresenta um modelo para problemas de ordem com restrições: CPSO(mais). Este modelo é a combinação de dois modelos da literatura, o PSO(mais), que apresenta bons resultados em problemas com restrições, e o CPSO, que introduz as modificações necessárias para aplicar o PSO em problemas de ordem. O modelo proposto foi adaptado para resolver o complexo problema de definir a melhor sequência de atividades embarcadas e funcionários alocados, de forma a maximizar o lucro da prestadora de serviço no período de três meses respeitando todas as restrições de prazo de conclusão dos serviços e restrições específicas do segmento offshore. Para avaliar o desempenho deste novo modelo na resolução do problema proposto, duas variantes do CPSO(mais) foram avaliadas frente ao modelo da literatura, CPSO, em seis casos de simulação propostos. Conclui-se pelos resultados das simulações que o modelo CPSO(mais) com inicialização reduzida destaca-se dos demais avaliados por apresentar um tempo de execução moderado e com soluções melhores que as dos demais. / [en] In order to develop an offshore maintenance support system, this work presents a model for constrained combinatorial problems: CPSO(plus). This model is a combination of two models, the PSO(plus), which presented good results in problems with constrains, and the CPSO, which is an adaptation of PSO for application in combinatorial problems. The proposed model has been adapted to solve the complex problem of defining the best sequence of offshore activities and allocated staff so as to maximize service provider profitability within three months while respecting all service completion time constraints and specific offshore work constraints. To evaluate the performance of this new model in solving the proposed problem, two CPSO(plus) variants were evaluated against the literature model, CPSO, in six proposed simulation cases. It is concluded from the results of the simulations that the CPSO(plus) model with reduced initialization outperforms other evaluated models with respect to execution time and solutions to given problem.

<p dir="ltr">Tracking and characterizing the space debris population in Earth orbit is necessary to ensure that space can continue to be used safely. However, because space objects are affected by non-conservative forces like drag and solar radiation pressure, predicting the long-term evolution of their orbits is impossible without knowledge of their attitude profiles. Such knowledge may be unavailable for inactive satellites or objects of which the observer is not the owner or operator. In many cases, attitude cannot be measured directly because resolved images of space objects are unavailable due to the distance between the object and the observer, and the effects of atmospheric seeing. However, the total brightness of objects can still be measured. A set of brightness measurements over time is referred to as a "light curve.'' An object's observed brightness is influenced by its attitude and other factors such as its orbit, shape, and reflective properties. If some of these other factors are known, attitude information may be extracted from a light curve. Existing methods of solving this attitude inversion problem either require a good initial guess for an object's rotational states or do not provide a full state estimate. The work in this thesis avoids both problems and provides a full state estimate without requiring an initial state guess.</p><p><br></p><p dir="ltr">The attitude estimation process assumes that the observation geometry and the observed object's shape, reflection properties, and inertia tensor are known. In this thesis, an initial method of searching for attitudes that could correspond to each measurement using the viewing sphere is described. These possible attitudes or "pseudo-measurements'' are then used to initialize a probability hypothesis density filter that is theoretically capable of representing the multi-modal nature of the attitude estimate using a Gaussian mixture model. However, the probability hypothesis density filter is found to often diverge from the truth because it is necessary to merge and prune components of the Gaussian mixture model to avoid computational intractability. In its place, a particle swarm optimizer method for performing an attitude inversion has been developed. This method uses analytic attitude solutions to quickly propagate a large number of attitude time histories simultaneously. The particle swarm optimizer method is validated using simulated light curves for several objects. A preliminary attempt is made to estimate the attitude of an object using real light curve measurements.</p>

This dissertation explores the applicability of RC helicopters as a tool to map wind conditions. This dissertation presents the construction of a robust instrumentation system capable of wireless in-situ measurement and mapping of ship airwake. The presented instrumentation system utilizes an RC helicopter as a carrier platform and uses the helicopter's dynamics for spatial 3D mapping of wind turbulence. The system was tested with a YP676 naval training craft to map ship airwake generated in controlled heading wind conditions. Novel system modeling techniques were developed to estimate the dynamics of an instrumented RC helicopter, in conjunction with onboard sensing, to estimate spatially varying (local) wind conditions. The primary problem addressed in this dissertation is the reliable estimation and separation of pilot induced dynamics from the system measurements, followed by the use of the dynamics residuals/discrepancies to map the wind conditions. This dissertation presents two different modelling approaches to quantify ship airwake using helicopter dynamics. The helicopter systems were characterized using both machine learning and analytical aerodynamic modelling approaches. In the machine learning based approaches, neural networks, along with other models, were trained then assessed in their capability to model dynamics from pilot inputs and other measured helicopter states. The dynamics arising from the wind conditions were fused with the positioning estimates of the helicopter to generate ship airwake maps which were compared against CFD generated airwake patterns. In the analytical modelling based approach, the dynamic response of an RC helicopter to a spatially varying parameterized wind field was modeled using a 30-state nonlinear ordinary differential equation-based dynamic system, while capturing essential elements of the helicopter dynamics. The airwake patterns obtained from both types of approach were compared against anemometrically produced wind maps of turbulent wind conditions artificially generated in a controlled indoor environment. Novel hardware architecture was developed to acquire data critical for the operation and calibration of the proposed system. The mechatronics design of three prototypes of the proposed system were presented and performance evaluated using experimental testing with a modified YP676 naval training vessel in the Chesapeake Bay area. In closing, qualitative analysis of these systems along with potential applications and improvements are discussed to conclude this dissertation. / Ph. D. / Ship airwake is a trail of wind turbulence left behind the superstructure of cruising naval vessels and are considered as a serious safety concern for aviators during onboard operations. Prior knowledge of the airwake distribution around the ship can alert pilots of possible hazards ahead of time and mitigate operational risks during the launch and recovery of the aircraft on the flight deck. This dissertation presents a novel application of Remote Control (RC) helicopters as tools to measure and map ship airwake. This dissertation presents two approaches to extract wind conditions from helicopter dynamics: (1) using machine learning based modeling, and (2) using analytic aerodynamic modeling-based estimation. Machine Learning is a modern engineering tool to model and simulate any system using experimental data alone. Under the machine learning based approach, the helicopter’s response to pilot inputs was modeled using multiple algorithms, with experimental flight data collected the absence of the ship airwake. With an assumption of capturing all the aerodynamic effects with the machine learning algorithms, the deviations in the dynamics estimates during testing environment were used to characterize and map ship airwake. In contrast to the machine learning model, the analytical approach modeled all critical aerodynamic processes of the RC helicopter as functions of pilot inputs and wind conditions using well defined physics laws, thus eliminating any need for training data. This approach predicts wind conditions on the basis of the model’s capability to match the estimates of helicopter dynamics to the actual measurements. Both presented approaches were tested on wind conditions created in indoor and outdoor environments. The performance of the proposed system was evaluated in experimental testing with a modified YP676 naval training vessel in the Chesapeake Bay area. The dissertation also presents the mechatronic design details of the novel hardware prototypes and subsystems used in the various studies and experiments. Finally, qualitative analysis of these systems along with their potential applications and improvements are discussed to conclude this dissertation.

Massive multiple input multiple output - MIMO systems are a reality and modern communication systems rely upon this technology to cope with the increasing need for capacity and network usage. Antenna arrays are at the heart of the of the massive-MIMO system and are the enabling technology. The defining cost of such a system is the number of transmit receive ports TRx as they dictate the number of control points and the associated digital control computational capacity. Typically users are spread along the azimuth and there is limited angular user spread along elevation. This enables us to group the elements in elevation which of course limits the elevation scanning performance. The element grouping result in grating lobes when we do elevation scanning. In the newly introduced frequency range 3 - FR3 in the envisioned 6G communication systems that is from 6-20 GHz it will not be allowed to transmit power above the horizon and the resulting grating lobes from the standard grouping should be mitigated. This project is structured into two parts. In the first part a grating lobe mitigation technique based on irregular subarray grouping utilizing the wellknown Penrose irregular tessellation is developed. This tessellation is based into two geometrical shapes where when put together they can fully tile the space aperiodically. Introducing this apperiodicity the grating or quantization lobes of the array are mitigated. In addition, in the first part a beam forming algorithm is developed based on particle swarm optimization that is able to produce the optimal weights for the array steering as well as optimize some of the embedded patterns of the irregular grouping. The last optimization step of the irregular subarray patterns is utilized only when the grouping results in a narrow pattern in azimuth and as a result we have static single port beamforming networks. This of course is a trade off between the broadside gain and the azimuth steerability of the array. In the second part of this thesis two low loss band pass filters have been developed with a PCB integrated suspended stripline techology. The filters were optimised for the frequencies within FR3. The resulted filtering structures can further be integrated at the input port of the proposed feeding network with the same technology. The two parts of this thesis target to introduce on one hand a antenna array architecture with subarray groupings that produce no grating lobes and on the other hand the proposed filtering structures have small enough dimensions to fit within the subarray footprint. / Dagens moderna kommunikationssystem använder sig av Massive multiple input multiple output (m-MIMO) för att kunna möta det allt större kraven på kapacitet och nätverksanvändning. Gruppantenner är den mest fundamentala delen av massive-MIMO system och möjliggör dess funktion. För ett sådant system (m-MIMO-system), så kommer den största kostnaden från antalet sändare/mottagare (TRx) -portar som används. Antalet portar i ett massiveMIMO system bestämmer vilken kapacitet systemet har till hands när det gäller lobformning. Vanligtvis är användare utspridda i det horisontella planet, samtidigt som de är begränsade i sin spridning i höjdled. Detta möjliggör användandet av en gruppantenn som grupperar sina antennelement i höjdled, vilket såklart begränsar gruppantennens lobformning i höjdled. Grupperandet av antennelement skapar sidlober när gruppantennen lobformar i höjdled. I det nya frekvensbandet, 3 - FR3 i det föreställda 6G kommunikationssystemet som opererar mellan 6-20 GHz, så kommer det inte att vara tillåtet att sända ut effekt över horisonten, samtidigt som de sidlober som kommer från standardgruppering måste begränsas. Detta projekt är strukturerat i två delar. I första delen så presenteras ett sätt att lindra sidlober, som baseras på irreguljära gruppantenner via Penrose tessellation. Denna tessellation är indelad i två geometriska former sådan att när vi sätter ihop dem så kan de framgångsrikt täcka vår geometri icke-periodvist. Genom att introducera denna icke-periodicitet så kan sidloberna från gruppanetnnen lindras. Utöver detta så är också så är en lobformningsalgoritm skapad som baseras på particle swarm optimization (PSO), som kan skapa de optimala vikterna för lobformning och lobstyrning. Det sista optimiseringssteget av de irreguljära gruppantennmönstret används bara när gruppering av antennelement resulterar i ett snävt mönster i azimut-riktning. Därför använder vi ett statiskt enportsmatningsnätverk. Detta är såklart en vägning mellan bredsideförstärkning och förmågan att kunna lobforma i det horisontella planet. I den andra delen så har två låg förlust bandpassfilter utvecklats med en PCB-integrerad suspended sripline teknik. Filtrerna optimerades för frekvenser inom FR3. De resulterande filterstrukturerna kan integreras längs input-porten av det föreslagna matningsnätverket som använder sig av den samma teknik. De två delarna i denna uppsats presenterar dels en gruppantenn med irreguljär antennelementsindelning som lindrar sidlober, samt dels filterstrukturer som kan användas tillsammans med gruppantennen.

Master of Science / Department of Electrical and Computer Engineering / Sanjoy Das / Stephen M. Welch / Multi-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optimal solutions due to the huge size of the search space. At the same time, the solution set produced by most of the currently used evolutionary algorithms lacks diversity. The present study investigates a new optimization method to solve multi-objective problems based on the widely used swarm-intelligence approach, Particle Swarm Optimization (PSO). Compared to other approaches, the proposed algorithm converges relatively fast while maintaining a diverse set of solutions. The investigated algorithm, Partially Informed Fuzzy-Dominance (PIFD) based PSO uses a dynamic network topology and fuzzy dominance to guide the swarm of dominated solutions. The proposed algorithm in this study has been tested on four benchmark problems and other real-world applications to ensure proper functionality and assess overall performance. The multi-objective gene regulatory network (GRN) problem entails the minimization of the coefficient of variation of modified photothermal units (MPTUs) across multiple sites along with the total sum of similarity background between ecotypes. The results throughout the current research study show that the investigated algorithm attains outstanding performance regarding optimization aspects, and exhibits rapid convergence and diversity.

Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Over the past 50 years smart materials have made their appearance in many structures. The thermopiezoelectric ceramic is one of these smart materials. When thermal e ects are considered negligible, then the materials are classified as piezo-ceramic and piezoelectric materials. These so called piezo-ceramics are used as actuator and sensor components in many structures. The use of these components with composite materials is significant due to their application in the aerospace and aeronautics fields. The interaction that the piezoelectric material has with a composite body can be improved in order to reduce the energy requirement of the material for deformation. An objective in the optimisation of composite material structures is to minimise compliance or maximise sti ness uT f, with the laminate ply orientations as design variables, where u and f are displacement and force vectors, respectively. Here, the objective is not the maximisation of sti ness but the maximisation of compliance, with typical constraints being failure criteria. These failure criteria can include theories such as the maximum principle stress, the Tsai-Hill or Tsai-Wu failure theories. The compliance is maximised to accentuate any piezoelectric movement and is for theoretical treatment only. Piezoelectric materials once polarized the materials becomes quasi-isotropic. The piezoelectric materials are isotropic in the plane normal to the direction of the voltage being applied and have altered properties normal to this plane. This change in the material properties can be exploited so that the layup can be altered in orientation to improve performance. The idea is to improve the mechanical capabilities of the structure subject to an electrical input or vice versa. In the works by both Carrera et al. and Piefort, First Order Shear Deformation Theory (FSDT) is used in finite element analysis to characterise the structural and electrical behaviour of a plate or shell. FSDT, also known as the Mindlin-Reissner theory, is a plate bending theory that assumes a transverse shear distribution through the thickness of the plate. This theory is considered an improvement on the standard theories such as the Kircho or Timoshenko theories. Many optimisation techniques exist and are classed as either being direct search or gradient based methods. Particle Swarm Optimisation (PSO) is a direct search method. It mimics the behaviour of a flock of birds or school of fish in their attempt to find food. The PSO’s mathematical statement characterises a set of initial unknown particles within a designated search space that are compared to a set of local best particles and a single global best particle. This comparison is used to update the swarm each run cycle. Regression is a procedure whereby a set of testing data is used to fit a pseudo-function that represents the form the data should take in practice. The aim of this work is to optimise the piezoelectric-composite layer interaction to improve the overall compliance of a structure. Extensive modelling is performed and tested with peer reviewed literature to demonstrate its accuracy. / AFRIKAANSE OPSOMMING: Oor die afgelope 50 jaar het slim materiale hulle verskyning gemaak in verskeie strukture. Termopiezo-elektriese keramieke is een van hierdie nuwe materiale. Wanneer termiese e ekte onbeduidend is, word hierdie materiale as piezo-elektriese materiale geklassifiseer. Hierdie sogenaamde piezo-keramieke word gebruik as aandrywers en sensoriese onderdele in verskeie strukture. Die kombinasie van hierdie onderdele met saamgestelde materiale het belangrike toepassings in die ruimte- en lugvaartkunde. Die interaksie van die piezo-elektriese materiale met die saamgestelde materiaal strukture kan verbeter word om die energie-vereistes van die materiaal vir vervorming te verminder. ’n Tipiese doel in die optimering van saamgestelde materiaalstrukture is om styfheid uT f te maksimeer met die gelamineerde laag-oriëntasies as ontwerpsveranderlikes, waar u en f onderskeidelik verplasing en kragvektor voorstel. In teenstelling met die optimering van die samestelling wat voorheen gedoen is, is die doel hier nie die maksimering van styfheid nie, maar die minimering van styfheid, met falingskriteria as tipiese beperkings. Die falingskriteria sluit die volgende in: die maksimum spanningsteorie, en die Tsai-Hill of Tsai-Wu falingsteorieë. Die styfheid word geminimeer om piezo-elektriese verplasing te versterk, maar word hierin net teoreties bekyk. Sodra piezo-elektriese materiale gepolariseer word, word hulle quasi-isotropies. Die piezoelektriese materiale is isotropies in die vlak gelyk aan die rigting van die stroomspanning wat daarop toegepas word en het ander eienskappe normaal tot die vlak. Die verandering in die materiaal se eienskappe kan gebruik word sodat beide die saamgestelde materiaal en die piezoelektriese laag se oriëntasie aangepas kan word vir verbeterde werkverrigting. Die idee is om die meganiese vermoëns te verbeter van ’n struktuur wat onderwerp word aan ’n elektriese inset of vice versa. In die literatuur van beide Carrera et al. en Piefort word Eerste Orde Skuifvervormings Teorie (EOST) gebruik in eindige element analises om die strukturele en elektriese gedrag van ’n plaat of dop te karakteriseer. EOST, ook bekend as Mindlin-Reissner teorie, is ’n plaat buigings-teorie wat ’n dwarsvervormingverspreiding aanneem deur die dikte van die plaat. Hierdie teorie word gesien as ’n verbetering op die standaard teorieë soos bv. Kircho of Timoshenko se teorieë. Daar bestaan baie optimeringstegnieke wat geklassifiseer word as ’direkte soek’ of ’hellinggebaseerde’ metodes. Partikel swerm-optimering (PSO) is ’n direkte soekmetode. Dit boots die gedrag van ’n swerm voëls of ’n skool visse in hulle poging om kos te vind, na. PSO se wiskundige stelling karakteriseer ’n aanvanklike stel onbekende partikels binne ’n afgebakende soekgebied wat vergelyk word met ’n stel van die beste plaaslike partikels sowel as ’n enkele beste globale partikel. Die vergelykings word gebruik om die swerm met elke siklus op te dateer. Regressie is ’n metode waarin toetsdata gebruik word om ’n benaderde funksie te konstrueer wat ongeveer voorspel hoe die regte funksie lyk. Die doel van hierdie werk is om die piezoelektriese saamgestelde laag te optimeer en die interaksie van die totale gedrag van die struktuur te verbeter. Uitgebreide modellering word uitgevoer en getoets met eweknie-beoordeelde literatuur om die akkuraatheid en korrektheid te bewys.

A successful Chemical Enhanced Oil Recovery (CEOR) program starts with a proper process selection for a given field, followed by a formulation of the batch components and a representative simulation step. Also, lab studies, field data, pilot testing, and economic analyses are required before project implementation. This work discusses the state of the art of the Surfactant-Polymer flood (SP) EOR technique, specifically for carbonate reservoirs, and states a methodology mixing laboratory, literature and reservoir simulation, to assess its applicability under economic and geological uncertainties. First, it is explained concepts related to the research, such as polymer, surfactant, microemulsion, functionalities of each chemical injected, advantages, and disadvantages. Second, a state of the art is developed about recent SP advances. Third, it is described the laboratory method being used to evaluate some properties of the chemicals injected for the Polymer flooding (PF) and SP flooding. Later, the simulation study step being conducted is explained, which will define the volume recovered and Net Present Value (NVP) obtained for the PF, SP injections and water flooding, in different economic and geological scenarios for two models resembling carbonate Brazilian reservoirs. Finally, it is discussed the results obtained, future researches that could be performed, and the respective bibliography. As part of this research, it was verified the Xanthan gum shows adequate results at different concentrations; that a surfactant specifically selected for a carbonate rock with low Interfacial tension and low adsorption is required; also that for the Lula based model although the polymer flooding and Surfactant-Polymer simulation brought some benefits, when compared with the waterflooding, on different economic scenarios and geological models, the high cost associated to the chemical handling facilities and volume spent do not make favorable its application in any scenario. On the contrary for the Cerena I field model, it was found the SP and Polymer flooding on all cases brought better results when compared with the water injection. Concluding that the performance and success of a CEOR program require finding the correct slug characteristics for the unique conditions of each reservoir. In this research the reservoir with higher production rates made possible the use of Chemical EOR presenting better results than a water injection however in the smaller model they were not economically viable due to the additional associated prices. / Um programa bem-sucedido de recuperação melhorada de petróleo por método químico (CEOR) começa com uma seleção precisa do processo para um determinado campo, seguido pela formulação dos componentes e uma etapa de formulação representativa. Adicionalmente, testes laboratoriais, dados de campos, testes pilotos e análises econômicas são necessárias antes da implementação de um projeto. Este trabalho discute o estado da arte da técnica de recuperação melhorada de petróleo (EOR) pela injeção de surfactante-polímero (SP), especificamente para reservatórios carbonáticos e, utilizada uma metodologia baseada em dados de laboratório, literatura e de simulação de reservatório para avaliar sua aplicabilidade sob incertezas econômicas e geológicas. Primeiramente, são explicados conceitos necessários a este trabalho relacionados com polímero, surfactante, microemulsão, funcionalidades de cada produto químico injetado, vantagens e desvantagens. Em segundo lugar, um estado da arte é desenvolvido sobre os avanços recentes do SP. Após, descreve-se os métodos laboratoriais utilizados para avaliar algumas propriedades dos produtos químicos usados nas injeções de Polímeros (PF) e SP. Posteriormente, é explicada a etapa do estudo de simulação, que definirá o volume recuperado e o valor presente líquido (NVP), obtidos para injeções PF, SP e água, em diferentes cenários econômicos e geológicos, para dois modelos semelhantes a reservatórios carbonáticos brasileiros. Por fim, são discutidos os resultados obtidos, sugestões de trabalhos futuros e apresentação da bibliografia. Como parte desta pesquisa, verificou-se que a goma xantana apresenta resultados consistentes em diferentes concentrações e que é necessário um surfactante especificamente selecionado para uma rocha carbonática, possuindo baixa tensão interfacial e baixa adsorção. Para o modelo baseado em Lula, embora a simulação de injeção de polímero e surfactante-polímero tenham trazido alguns benefícios, quando comparados com a injeção de água, em diferentes cenários econômicos e modelos geológicos, o alto custo associado às instalações de manipulação química e volume gasto não favorece sua aplicação em qualquer cenário. Por outro lado, no modelo de campo Cerena I, verificou-se que as injeções de SP e de polímero, em todos os casos, trouxeram melhores resultados quando comparadas com a injeção de água. Concluindo, o desempenho e o sucesso de um programa de CEOR exige encontrar as corretas características de slugs para condições únicas de cada reservatório. Neste trabalho, o reservatório com maiores taxas de produção infere que o método químico de EOR apresente melhores resultados quando comparado com a injeção de água.

Les métaheuristiques sont une famille d'algorithmes stochastiques destinés à résoudre des problèmes d 'optimisation difficile . Utilisées dans de nombreux domaines, ces méthodes présentent l'avantage d'être généralement efficaces, sans pour autant que l'utilisateur ait à modifier la structure de base de l'algorithme qu'il utilise. Parmi celles-ci, l'Optimisation par Essaim Particulaire (OEP) est une nouvelle classe d'algorithmes proposée pour résoudre les problèmes à variables continues. Les algorithmes d'OEP s'inspirent du comportement social des animaux évoluant en essaim, tels que les oiseaux migrateurs ou les poissons. Les particules d'un même essaim communiquent de manière directe entre elles tout au long de la recherche pour construire une solution au problème posé, en s'appuyant sur leur expérience collective. Reconnues depuis de nombreuses années pour leur efficacité, les métaheuristiques présentent des défauts qui rebutent encore certains utilisateurs. Le réglage des paramètres des algorithmes est un de ceux-ci. Il est important, pour chaque probléme posé, de trouver le jeu de paramètres qui conduise à des performances optimales de l'algorithme. Cependant, cette tâche est fastidieuse et coûteuse en temps, surtout pour les utilisateurs novices. Pour s'affranchir de ce type de réglage, des recherches ont été menées pour proposer des algorithmes dits adaptatifs . Avec ces algorithmes, les valeurs des paramètres ne sont plus figées, mais sont modifiées, en fonction des résultats collectés durant le processus de recherche. Dans cette optique-là, Maurice Clerc a proposé TRIBES, qui est un algorithme d'OEP mono-objectif sans aucun paramètre de contrôle. Cet algorithme fonctionne comme une boite noire , pour laquelle l'utilisateur n'a qu'à définir le problème à traiter et le critàre d'arrêt de l'algorithme. Nous proposons dans cette thèse une étude comportementale de TRIBES, qui permet d'en dégager les principales qualités et les principaux défauts. Afin de corriger certains de ces défauts, deux modules ont été ajoutés à TRIBES. Une phase d'initialisation régulière est insérée, afin d'assurer, dès le départ de l'algorithme, une bonne couverture de l'espace de recherche par les particules. Une nouvelle stratégie de déplacement, basée sur une hybridation avec un algorithme à estimation de distribution, est aussi définie, afin de maintenir la diversité au sein de l'essaim, tout au long du traitement. Le besoin croissant de méthodes de résolution de problèmes multiobjectifs a conduit les concepteurs à adapter leurs méthodes pour résoudre ce type de problème. La complexité de cette opération provient du fait que les objectifs à optimiser sont souvent contradictoires. Nous avons élaboré une version multiobjectif de TRIBES, dénommée MO-TRIBES. Nos algorithmes ont été enfin appliqués à la résolution de problèmes de seuillage d'images médicales et au problème de dimensionnement de composants de circuits analogiques / Metaheuristics are a new family of stochastic algorithms which aim at solving difficult optimization problems. Used to solve various applicative problems, these methods have the advantage to be generally efficient on a large amount of problems. Among the metaheuristics, Particle Swarm Optimization (PSO) is a new class of algorithms proposed to solve continuous optimization problems. PSO algorithms are inspired from the social behavior of animals living in swarm, such as bird flocks or fish schools. The particles of the swarm use a direct way of communication in order to build a solution to the considered problem, based on their collective experience. Known for their e ciency, metaheuristics show the drawback of comprising too many parameters to be tuned. Such a drawback may rebu some users. Indeed, according to the values given to the parameters of the algorithm, its performance uctuates. So, it is important, for each problem, to nd the parameter set which gives the best performance of the algorithm. However, such a problem is complex and time consuming, especially for novice users. To avoid the user to tune the parameters, numerous researches have been done to propose adaptive algorithms. For such algorithms, the values of the parameters are changed according to the results previously found during the optimization process. TRIBES is an adaptive mono-objective parameter-free PSO algorithm, which was proposed by Maurice Clerc. TRIBES acts as a black box , for which the user has only the problem and the stopping criterion to de ne. The rst objective of this PhD is to make a global study of the behavior of TRIBES under several conditions, in order to determine the strengths and drawbacks of this adaptive algorithm. In order to improve TRIBES, two new strategies are added. First, a regular initialization process is defined in order to insure an exploration as wide as possible of the search space, since the beginning of the optimization process. A new strategy of displacement, based on an hybridation with an estimation of distribution algorithm, is also introduced to maintain the diversity in the swarm all along the process. The increasing need for multiobjective methods leads the researchers to adapt their methods to the multiobjective case. The di culty of such an operation is that, in most cases, the objectives are con icting. We designed MO-TRIBES, which is a multiobjective version of TRIBES. Finally, our algorithms are applied to thresholding segmentation of medical images and to the design of electronic components