Dissertations / Theses on the topic 'Evolutionary structural optimisation'

In der vorliegenden Arbeit werden zwei Modelle zur strukturellen Analyse von Stoffwechselsystemen vorgestellt. Die Untersuchung basiert auf der Hypothese, dass heutzutage vorzufindende Stoffwechselsysteme als Ergebnis einer evolutionären Entwicklung, bestimmt durch Mutationsmechanismen und natürlicher Selektion, angesehen werden können. Es kann daher angenommen werden, dass kinetische Parameter sowie strukturelle Eigenschaften im Laufe der Evolution solche Werte angenommen haben, die eine gewisse Optimalität bezüglich ihrer biologischen Funktion darstellen. Das erste Modell untersucht das strukturelle Design ATP und NADH produzierender Systeme, so wie die Glykolyse und der Zitratzyklus. Eine Methode wird präsentiert, die die Beschreibung hypothetischer, chemisch denkbarer, alternativer Stoffwechselwege ermöglicht. Diese Wege werden bezüglich ihrer Effizienz, ATP zu produzieren, untersucht. Es stellt sich heraus, dass die meisten möglichen Wege eine niedrige ATP-Produktionsrate aufweisen und dass die effizientesten Wege einige strukturelle Gemeinsamkeiten besitzen. Die Optimierung bezüglich der ATP-Produktionsrate wird mit einem evolutionären Algorithmus durchgeführt. Folgende Resultate stehen mit dem tatsächlichen Design der Glykolyse und des Zitratzyklus in Einklang: (i) In allen effizienten Wegen befinden sich die ATP-verbrauchenden Reaktionen am Anfang. (ii) In allen effizienten Wegen befinden sich die sowohl die NADH- als auch die ATP-produzierenden Reaktionen am Ende. (iii) Die Anzahl der NADH-Moleküle, die aus einem energiereichen Molekül (Glukose) produziert werden, beläuft sich in allen effizienten Wegen auf vier. Im zweiten Modell werden vollständige Mengen metabolischer Netzwerke konstruiert, wobei von Reaktionen ausgegangen wird, die Änderungen des Kohlenstoffskeletts der beteiligten Metabolite beschreiben. Elementare Netzwerke werden dadurch definiert, dass eine bestimmte chemische Umwandlung durchgeführt werden kann und dass diese Fähigkeit verloren geht, wenn eine der beteiligten Reaktionen ausgeschlossen wird. Übergänge zwischen Netzwerken und Mutationen werden durch den Austausch einer einzigen Reaktion definiert. Es existieren verschiedene Mutationen, solche bei denen Funktionen verloren gehen, welche dazugewonnen werden, und neutrale Mutationen. Mutationen definieren Nachbarschaftsrelationen, die graphentheoretisch beschrieben werden. Eigenschaften wie Durchmesser, Konnektivität und die Abstandsverteilung der Vertizes werden berechnet. Ein Konzept zur Quantifizierung der Robustheit von Netzwerken gegenüber stöchiometrischen Veränderungen wird entwickelt, wobei zwischen starker und schwacher Robustheit unterschieden wird. Evolutionäre Algorithmen werden angewandt, um die Entwicklung von Netzwerkpopulationen unter konstanten und zeitlich veränderlichen Umweltbedingungen zu untersuchen. Es wird gezeigt, dass Populationen sich zu Gruppierungen von Netzwerken hinentwickeln, die gemeinsame Funktionen besitzen und nah benachbart sind. Unter zeitlich veränderlichen Umweltbedingungen zeigt sich, dass multifunktionelle Netzwerke optimal sind und sich im Selektionsprozess durchsetzen.<br>In the present thesis two models are presented which study the structural design of metabolic systems. The investigation is based on the hypothesis that present day metabolic systems are the result of an evolutionary development governed by mutation mechanisms and natural selection principles. Therefore, it can be assumed that these parameters have reached, during the course of their evolution, values which imply certain optimal properties with respect to their biological function. The first model concerns the structural design of ATP and NADH producing systems such as glycolysis and the citric acid cycle. A method is presented to describe hypothetical, chemically feasible, alternative pathways. We analyse these pathways with respect to their capability to efficiently produce ATP. It is shown that most of the possible pathways result in a very low ATP production rate and that the very efficient pathways share common structural properties. Optimisation with respect to the ATP production rate is performed by an evolutionary algorithm. The following results of our analysis are in close correspondence to the real design of glycolysis and the TCA cycle: (i) In all efficient pathways the ATP consuming reactions are located near the beginning. (ii) In all efficient pathways NADH producing reactions as well as ATP producing reactions are located near the end. (iii) The number of NADH molecules produced by the consumption of one energy-rich molecule (glucose) amounts to four in all efficient pathways. In the second model complete sets of metabolic networks are constructed starting from a limited set of reactions describing changes in the carbon skeleton of biochemical compounds. Elementary networks are defined by the condition that a specific chemical conversion can be performed by a set of given reactions and that this ability will be lost by elimination of any of these reactions. Transitions between networks and mutations of networks are defined by exchanges of single reactions. Different mutations exist such as gain or loss of function mutations and neutral mutations. Based on these mutations neighbourhood relations between networks are established which are described in a graph theoretical way. Basic properties of these graphs are determined such as diameter, connectedness, distance distribution of pairs of vertices. A concept is developed to quantify the robustness of networks against changes in their stoichiometry where we distinguish between strong and weak robustness. Evolutionary algorithms are applied to study the development of network populations under constant and time dependent environmental conditions. It is shown that the populations evolve toward clusters of networks performing a common function and which are closely neighboured. Under changing environmental conditions multifunctional networks prove to be optimal and will be selected.

The overall objective of this research was to realise the practical application of Hierarchical Asynchronous Parallel Evolutionary Algorithms for Multi-objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using high fidelity analysis tools. The research looked at the assumed aerodynamics and structures of two production UAV wings and attempted to optimise these wings in isolation to the rest of the vehicle. The project was sponsored by the Asian Office of the Air Force Office of Scientific Research under contract number AOARD-044078. The two vehicles wings which were optimised were based upon assumptions made on the Northrop Grumman Global Hawk (GH), a High Altitude Long Endurance (HALE) vehicle, and the General Atomics Altair (Altair), Medium Altitude Long Endurance (MALE) vehicle. The optimisations for both vehicles were performed at cruise altitude with MTOW minus 5% fuel and a 2.5g load case. The GH was assumed to use NASA LRN 1015 aerofoil at the root, crank and tip locations with five spars and ten ribs. The Altair was assumed to use the NACA4415 aerofoil at all three locations with two internal spars and ten ribs. Both models used a parabolic variation of spar, rib and wing skin thickness as a function of span, and in the case of the wing skin thickness, also chord. The work was carried out by integrating the current University of Sydney designed Evolutionary Optimiser (HAPMOEA) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) tools. The variable values computed by HAPMOEA were subjected to structural and aerodynamic analysis. The aerodynamic analysis computed the pressure loads using a Boeing developed Morino class panel method code named PANAIR. These aerodynamic results were coupled to a FEA code, MSC.Nastran® and the strain and displacement of the wings computed. The fitness of each wing was computed from the outputs of each program. In total, 48 design variables were defined to describe both the structural and aerodynamic properties of the wings subject to several constraints. These variables allowed for the alteration of the three aerofoil sections describing the root, crank and tip sections. They also described the internal structure of the wings allowing for variable flexibility within the wing box structure. These design variables were manipulated by the optimiser such that two fitness functions were minimised. The fitness functions were the overall mass of the simulated wing box structure and the inverse of the lift to drag ratio. Furthermore, six penalty functions were added to further penalise genetically inferior wings and force the optimiser to not pass on their genetic material. The results indicate that given the initial assumptions made on all the aerodynamic and structural properties of the HALE and MALE wings, a reduction in mass and drag is possible through the use of the HAPMOEA code. The code was terminated after 300 evaluations of each hierarchical level due to plateau effects. These evolutionary optimisation results could be further refined through a gradient based optimiser if required. Even though a reduced number of evaluations were performed, weight and drag reductions of between 10 and 20 percent were easy to achieve and indicate that the wings of both vehicles can be optimised.

Master of Engineering (Research)<br>The overall objective of this research was to realise the practical application of Hierarchical Asynchronous Parallel Evolutionary Algorithms for Multi-objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using high fidelity analysis tools. The research looked at the assumed aerodynamics and structures of two production UAV wings and attempted to optimise these wings in isolation to the rest of the vehicle. The project was sponsored by the Asian Office of the Air Force Office of Scientific Research under contract number AOARD-044078. The two vehicles wings which were optimised were based upon assumptions made on the Northrop Grumman Global Hawk (GH), a High Altitude Long Endurance (HALE) vehicle, and the General Atomics Altair (Altair), Medium Altitude Long Endurance (MALE) vehicle. The optimisations for both vehicles were performed at cruise altitude with MTOW minus 5% fuel and a 2.5g load case. The GH was assumed to use NASA LRN 1015 aerofoil at the root, crank and tip locations with five spars and ten ribs. The Altair was assumed to use the NACA4415 aerofoil at all three locations with two internal spars and ten ribs. Both models used a parabolic variation of spar, rib and wing skin thickness as a function of span, and in the case of the wing skin thickness, also chord. The work was carried out by integrating the current University of Sydney designed Evolutionary Optimiser (HAPMOEA) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) tools. The variable values computed by HAPMOEA were subjected to structural and aerodynamic analysis. The aerodynamic analysis computed the pressure loads using a Boeing developed Morino class panel method code named PANAIR. These aerodynamic results were coupled to a FEA code, MSC.Nastran® and the strain and displacement of the wings computed. The fitness of each wing was computed from the outputs of each program. In total, 48 design variables were defined to describe both the structural and aerodynamic properties of the wings subject to several constraints. These variables allowed for the alteration of the three aerofoil sections describing the root, crank and tip sections. They also described the internal structure of the wings allowing for variable flexibility within the wing box structure. These design variables were manipulated by the optimiser such that two fitness functions were minimised. The fitness functions were the overall mass of the simulated wing box structure and the inverse of the lift to drag ratio. Furthermore, six penalty functions were added to further penalise genetically inferior wings and force the optimiser to not pass on their genetic material. The results indicate that given the initial assumptions made on all the aerodynamic and structural properties of the HALE and MALE wings, a reduction in mass and drag is possible through the use of the HAPMOEA code. The code was terminated after 300 evaluations of each hierarchical level due to plateau effects. These evolutionary optimisation results could be further refined through a gradient based optimiser if required. Even though a reduced number of evaluations were performed, weight and drag reductions of between 10 and 20 percent were easy to achieve and indicate that the wings of both vehicles can be optimised.

L’objectif de cette thèse est le développement de méthodes d’optimisation multi-objectif pour la résolution de problèmes de conception des structures mécaniques. En effet, la plupart des problèmes réels dans le domaine de la mécanique des structures ont plusieurs objectifs qui sont souvent antagonistes. Il s’agit, par exemple, de concevoir des structures en optimisant leurs poids, leurs tailles, et leurs coûts de production. Le but des méthodes d’optimisation multi-objectif est la recherche des solutions de compromis entre les objectifs étant donné l’impossibilité de satisfaire tout simultanément. Les métaheuristiques sont des méthodes d’optimisation capables de résoudre les problèmes d’optimisation multi-objective en un temps de calcul raisonnable sans garantie de l’optimalité de (s) solution (s). Au cours des dernières années, ces algorithmes ont été appliqués avec succès pour résoudre le problème des mécaniques des structures. Dans cette thèse deux métaheuristiques ont été développées pour la résolution des problèmes d’optimisation multi-objectif en général et de conception de structures mécaniques en particulier. Le premier algorithme baptisé MOBSA utilise les opérateurs de croisement et de mutation de l’algorithme BSA. Le deuxième algorithme nommé NNIA+X est une hybridation d’un algorithme immunitaire et de trois croisements inspirés de l’opérateur de croisement original de l’algorithme BSA. Pour évaluer l’efficacité et l’efficience de ces deux algorithmes, des tests sur quelques problèmes dans littérature ont été réalisés avec une comparaison avec des algorithmes bien connus dans le domaine de l’optimisation multi-objectif. Les résultats de comparaison en utilisant des métriques très utilisées dans la littérature ont démontré que ces deux algorithmes peuvent concurrencer leurs prédécesseurs<br>The objective of this thesis is the development of multi-objective optimization methods for solving mechanical design problems. Indeed, most of the real problems in the field of mechanical structures have several objectives that are often antagonistic. For example, it is about designing structures by optimizing their weight, their size, and their production costs. The goal of multi-objective optimization methods is the search for compromise solutions between objectives given the impossibility to satisfy all simultaneously. Metaheuristics are optimization methods capable of solving multi-objective optimization problems in a reasonable calculation time without guaranteeing the optimality of the solution (s). In recent years, these algorithms have been successfully applied to solve the problem of structural mechanics. In this thesis, two metaheuristics have been developed for the resolution of multi-objective optimization problems in general and of mechanical structures design in particular. The first algorithm called MOBSA used the crossover and mutation operators of the BSA algorithm. The second one named NNIA+X is a hybridization of an immune algorithm and three crossover inspired by the original crossover operator of the BSA algorithm. To evaluate the effectiveness and efficiency of these two algorithms, tests on some problems in literature have been made with a comparison with algorithms well known in the field of multi-objective optimization. The comparison results using metrics widely used in the literature have shown that our two algorithms can compete with their predecessors

La thèse se concentre sur une recherche méthodologique sur l'optimisation structurelle catégorielle au moyen d'un apprentissage multiple. Dans cette thèse, les variables catégorielles non ordinales sont traitées comme des variables discrètes multidimensionnelles. Afin de réduire la dimensionnalité, les nombreuses techniques d'apprentissage sont introduites pour trouver la dimensionnalité intrinsèque et mapper l'espace de conception d'origine sur un espace d'ordre réduit. Les mécanismes des techniques d'apprentissage à la fois linéaires et non linéaires sont d'abord étudiés. Ensuite, des exemples numériques sont testés pour comparer les performances de nombreuses techniques d’apprentissage. Sur la base de la représentation d'ordre réduit obtenue par Isomap, les opérateurs de mutation et de croisement évolutifs basés sur les graphes sont proposés pour traiter des problèmes d'optimisation structurelle catégoriels, notamment la conception du dôme, du cadre rigide de six étages et des structures en forme de dame. Ensuite, la méthode de recherche continue consistant à déplacer des asymptotes est exécutée et fournit une solution compétitive, mais inadmissible, en quelques rares itérations. Ensuite, lors de la deuxième étape, une stratégie de recherche discrète est proposée pour rechercher de meilleures solutions basées sur la recherche de voisins. Afin de traiter le cas dans lequel les instances de conception catégorielles sont réparties sur plusieurs variétés, nous proposons une méthode d'apprentissage des variétés k-variétés basée sur l'analyse en composantes principales pondérées<br>The thesis concentrates on a methodological research on categorical structural optimizationby means of manifold learning. The main difficulty of handling the categorical optimization problems lies in the description of the categorical variables: they are presented in a category and do not have any orders. Thus the treatment of the design space is a key issue. In this thesis, the non-ordinal categorical variables are treated as multi-dimensional discrete variables, thus the dimensionality of corresponding design space becomes high. In order to reduce the dimensionality, the manifold learning techniques are introduced to find the intrinsic dimensionality and map the original design space to a reduced-order space. The mechanisms of both linear and non-linear manifold learning techniques are firstly studied. Then numerical examples are tested to compare the performance of manifold learning techniques mentioned above. It is found that the PCA and MDS can only deal with linear or globally approximately linear cases. Isomap preserves the geodesic distances for non-linear manifold however, its time consuming is the most. LLE preserves the neighbour weights and can yield good results in a short time. KPCA works like a non-linear classifier and we proves why it cannot preserve distances or angles in some cases. Based on the reduced-order representation obtained by Isomap, the graph-based evolutionary crossover and mutation operators are proposed to deal with categorical structural optimization problems, including the design of dome, six-story rigid frame and dame-like structures. The results show that the proposed graph-based evolutionary approach constructed on the reduced-order space performs more efficiently than traditional methods including simplex approach or evolutionary approach without reduced-order space. In chapter 5, the LLE is applied to reduce the data dimensionality and a polynomial interpolation helps to construct the responding surface from lower dimensional representation to original data. Then the continuous search method of moving asymptotes is executed and yields a competitively good but inadmissible solution within only a few of iteration numbers. Then in the second stage, a discrete search strategy is proposed to find out better solutions based on a neighbour search. The ten-bar truss and dome structural design problems are tested to show the validity of the method. In the end, this method is compared to the Simulated Annealing algorithm and Covariance Matrix Adaptation Evolutionary Strategy, showing its better optimization efficiency. In chapter 6, in order to deal with the case in which the categorical design instances are distributed on several manifolds, we propose a k-manifolds learning method based on the Weighted Principal Component Analysis. And the obtained manifolds are integrated in the lower dimensional design space. Then the method introduced in chapter 4 is applied to solve the ten-bar truss, the dome and the dame-like structural design problems

Though lots of numerical methods have been proposed in the literature to optimize me-chanical structures at the final stage of the design process, few designers use these tools since the first stage. However, a minor modification at the first step can bring significant change to the global performances of the structure. Usually, during the initial stage, models are based on theoretical and empirical equations, which are often characterized by mixed variables: continuous (e.g. geometrical dimensions), discrete (e.g. the cross section of a beam available in a catalogue) and/or integer (e.g. the number of layers in a composite material). Furthermore, the functions involved may be non differentiable, or even discontinuous. Therefore, classical algorithms based on the computation of sensi-tivities are no more applicable. <p><p>Consequently, to solve these problems, the most wide-spread meta-heuristic methods are evolutionary algorithms (EAs), which work as follows: the best individuals among an initial population of randomly generated potential solutions are favoured and com-bined (by specific operators like crossover and mutation) in order to create potentially better individuals at the next generation. The creation of new generations is repeated till the convergence is reached. The ability of EAs to explore widely the design space is useful to solve single-objective unconstrained optimization problems, because it gener-ally prevents from getting trapped into a local optimum, but it is also well known that they do not perform very efficiently in the presence of constraints. Furthermore, in many industrial applications, multiple objectives are pursued together. <p><p>Therefore, to take into account the constrained and multicriteria aspects of optimization problems in EAs, a new method called PAMUC (Preferences Applied to MUltiobjectiv-ity and Constraints) has been proposed in this dissertation. First the user has to assign weights to the m objectives. Then, an additional objective function is built by linearly aggregating the normalized constraints. Finally, a multicriteria decision aid method, PROMETHEE II, is used in order to rank the individuals of the population following the m+1 objectives. <p><p>PAMUC has been validated on standard multiobjective test cases, as well as on the pa-rametrical optimization of the purge valve and the feed valve of the Vinci engine, both designed by Techspace Aero for launcher Ariane 5.<p>\<br>Doctorat en sciences appliquées<br>info:eu-repo/semantics/nonPublished

This thesis attempts to solve the problem of optimising the design of tree structured centralized data network using an Evolutionary Algorithm. A centralized data network is also known as a client-server network. In this type of network, the client, which is usually a terminal connected to the network, would send a request for information to the server. The server would then download the reply back to the client. An example of such a network would be a bank's ATM network. Each ATM machine would be a client and the central server would store information relating to all the bank's customers. The idea was that once this was done the fitness function used in the above problem would be modified to suite the design of a network used to interconnect LANs that would also form a tree structure. Each of the nodes in this network would be a LAN connected to the network via a bridge or router. Unfortunately the results obtained in attempting to optimise the topology of the centralized data network were very poor. A heuristic normally used to solve this problem outperformed the Evolutionary Algorithm on all the three counts that the comparison was performed. Therefore another method using an Evolutionary Algorithm that can optimise the network interconnecting LANs was introduced. The first chapter in this thesis is an introduction to the thesis and all the terms and concepts that are used in it. The second chapter explains the heuristic used. The third chapter discusses what particular properties are needed by a coding scheme used in an Evolutionary Algorithm to solve this problem. It introduces a few alternatives that have been used in the past but do not meet all the requirements. Then it introduces the coding scheme that was used in this thesis and the fitness function used to evaluate each candidate solution. The next chapter tabulates the results and draws conclusions from these results. The final chapter discusses areas of future research possibilities. There are also several appendices. The first introduces the Genetic Algorithm (GA) and discusses some hypotheses that attempt to explain why it is so successful at problem solving. The next appendix introduces Population Based Incremental Learning (PBIL). This is the Evolutionary Algorithm that is used in attempting to solve this problem. Appendix C explains a method of converting between real and binary numbers; this method is not used in this thesis but is important to know when dealing with Evolutionary Algorithms that are only capable of manipulating binary values. The next two appendices discuss Prim's algorithm and Competitive Learning. Prim's algorithm is an MST algorithm that is used in the coding scheme. Competitive Learning is a classification technique that PBIL is partly based on. An explanation of each function used to implement the heuristic and PBIL is given in Appendix F. This is followed by a listing of the Matlab code of each function.

Process Algebras are a Formal Modelling methodology which are an effective tool for defining models of complex systems, particularly those involving multiple interacting processes. However, describing such a model using Process Algebras requires expertise from both the modeller and the domain expert. Finding the correct model to describe a system can be difficult. Further more, even with the correct model, parameter tuning to allow model outputs to match experimental data can also be both difficult and time consuming. Evolutionary Algorithms provide effective methods for finding solutions to optimisation problems with large and noisy search spaces. Evolutionary Algorithms have been proven to be well suited to investigating parameter fitting problems in order to match known data or desired behaviour. It is proposed that Process Algebras and Evolutionary Algorithms have complementary strengths for developing models of complex systems. Evolutionary Algorithms require a precise and accurate fitness function to score and rank solutions. Process Algebras can be incorporated into the fitness function to provide this mathematical score. Presented in this work is the Evolving Process Algebra (EPA) framework, designed for the application of Evolutionary Algorithms (specifically Genetic Algorithms and Genetic Programming optimisation techniques) to models described in Process Algebra (specifically PEPA and Bio-PEPA) with the aim of evolving fitter models. The EPA framework is demonstrated using multiple complex systems. For PEPA this includes the dining philosophers resource allocation problem, the repressilator genetic circuit, the G-protein cellular signal regulators and two epidemiological problems: HIV and the measles virus. For Bio-PEPA the problems include a biochemical reactant-product system, a generic genetic network, a variant of the G-protein system and three epidemiological problems derived from the measles virus. Also presented is the EPA Utility Assistant program; a lightweight graphical user interface. This is designed to open the full functionality and parallelisation of the EPA framework to beginner or naive users. In addition, the assistant program aids in collating and graphing after experiments are completed.

Cette thèse aborde la problématique de la conception optimale de structures composites stratifiées d’épaisseur variable. Les variables d’empilement définissent un problème d’optimisation combinatoire et des espaces de décisions de grande taille et potentiellement multimodaux. Les algorithmes d’optimisation stochastiques permettent de traiter ce type de problème et de tirer profit des performances et de l’anisotropie des plis composites pour l’allègement des structures composites stratifiées. Le but de cette étude est double : (i) développer un algorithme d’optimisation dédié aux composites stratifiés d’épaisseur variable et (ii) estimer le potentiel des composites stratifiés pour la maîtrise des performances aérodynamiques d’une pale de CROR composite.Dans la première partie de cette thèse, un algorithme évolutionnaire est spécialisé pour l’optimisation de tables de drapage et la gestion d’un ensemble de règles de conception représentatif des pratiques de l’industrie. Pour se faire, un encodage spécifique des solutions est proposé et des opérateurs de variations spécialisés sont développés.Dans la deuxième partie, l’algorithme est enrichi d’une technique de guidage basée sur l’exploitation d’un espace auxiliaire afin d'accroître son efficacité et d’intégrer davantage de connaissances des composites dans la résolution du problème.Finalement, la méthode est appliquée pour la conception d’une pale de CROR composite à l’échelle de la maquette de soufflerie. Au préalable, des processus itératifs de mise à froid et mise à chaud de la pale sont mis en place afin d’estimer la forme de la pale au repos et l’état de contraintes dans la pale en fonctionnement<br>This thesis deals with the optimal design of variable-thickness laminated composite structures. The stacking variables define a combinatorial optimization problem and large decision spaces which are potentially multimodal. Stochastic optimization algorithms allow solving this type of problem and allow taking advantage from the performance and the anisotropic nature of unidirectional composite plies to lighten laminated composite structures.The purpose of this study is twofold: (i) developing an optimization algorithm dedicated to variable-thickness laminated composites and (ii) assessing the potential of laminated composites in influencing the aerodynamic performances of a composite CROR blade.Firstly, an evolutionary algorithm is specialized in order to optimize layup tables and handle a set of design guidelines which is representative of industrial practices. In this purpose, a specific encoding of the solutions is suggested and specialized variation operators are developed.Secondly, the algorithm is enriched with a guiding technique based on the exploitation of an auxiliary space in order to improve its efficiency and to include further composites-related knowledge for the resolution of the problem.Finally, the method is applied for the design of a reduced-scale composite CROR blade intended for wind-tunnel testing. Beforehand, iterative processes are implemented to estimate the shape of the non-operating blade and the stress state within the operating blade

La conception des systèmes d'entrainement de bande est étudiée depuis de nombreuses années. Ces systèmes sont très répandus dans l'industrie puisque le conditionnement sous forme de bobines simplifie le traitement de nombreux matériaux tels que le papier, le carton, les polymères, ... Ces systèmes regroupent un grand nombre de difficultés : ils sont de grande dimension, un fort couplage existe entre les grandeurs et de nombreux paramètres varient au cours du temps. La méthode d’optimisation classique consiste à considérer chaque partie du système sans tenir compte des autres. De plus, au sein d'un même sous-ensemble, l’optimisation est réalisée discipline par discipline. Une nouvelle approche est proposée et appliquée à la synthèse de la commande : il s’agit de considérer le système global en prenant en compte la robustesse paramétrique. Ensuite, le choix optimal du tracteur maître et de la technologie d'asservissement de la tension de bane associée sont étudiées<br>The design of roll-to-roll systems is studied for several years. This kind of system is very common in industry because the wound roll packaging simplify the treatment of material such as paper, cardboard, polymers, metal ... The studied systems have a high number of difficulties: they are large scale systems, a high coupling between physical values exists and several parameters are time dependent. The classical optimisation method consists in considering separately each subsystem without taking into account interactions. Moreover, the classical optimisation is made for each scientific field. A new approach is developed and applied to controller synthesis: the controllers are synthesized considering the global system with parametric uncertainties. The optimal choice of the master roller position and the technology used to control web tension are then studied

Dans ces travaux, nous nous concentrons sur l’amélioration de l’accessibilité web pourdes utilisateurs handicapés visuels à travers d’outils s’intègrant dans la plateforme SmartWeb Accessibility Platform (SWAP). Après une synthèse sur l’accessibilité, un panoramade SWAP est présenté. Notre première contribution concerne la compensation de la pertede contraste textuel dans les pages web pour les daltoniens tout en maintenant les intentionsde l’auteur retranscrites par les couleurs. Ce problème est modélisé sous la forme d’unproblème de minimisation d’une fonction numérique dépendante des couleurs initiales, desrelations entre les couleurs de la page et des couleurs modifiées. L’intérêt et l’efficacité detrois méthodes d’optimisation (mass-spring system, CMA-ES, API) ont été évaluées surdeux jeux de données (réel, artificiel). Notre deuxième contribution cherche à améliorer lastructure de pages web pour des utilisateurs de lecteurs d’écran afin de compenser la perted’information due à la linéarisation du contenu. Grâce à l’utilisation d’heuristiques et detechnique d’apprentissage, les zones caractéristiques de la page sont identifiées. La pageest complétée en utilisant des liens supplémentaires et les marqueurs ARIA afin de permettreune meilleure identification des zones par les lecteurs d’écran. Nos expérimentationsmontrent l’efficacité de notre proposition<br>This thesis works are focused on enhancing web accessibility for users with visual disabilities using tools integrated within the SmartWeb Accessibility Platform (SWAP). After a synthesis on accessibility, SWAP is presented. Our first contribution consists in reducing the contrast loss for textual information in web pages for dichromat users while maintaining the author’s intentions conveyed by colors. The contrast compensation problem is reduced at minimizing a fitness function which depends on the original colors and the relationships between them. The interest and efficiency of three methods (mass-spring system, CMA-ES, API) are assessed on two datasets (real and artificial). The second contribution focuses on enhancing web page structure for screen reader users in order to overcome the effect of contents’linearization. Using heuristics and machine learning techniques, the main zones of the page are identified. The page structure can be enhanced using ARIA statements and access links to improve zone identification by screen readers

Le pilotage en temps réel des systèmes de production nécessite de prendre des décisions complexes sur l'affectation des ressources ou le choix des tâches à réaliser. Compte tenu de l'importance de la pertinence des décisions pour la performance d'un atelier, le pilotage fait l'objet de travaux de recherche dont l'objectif est d'aider les preneurs de décision. En particulier, on ne sait pas évaluer les conséquences sur la performance d'une décision en temps réel car les bonnes performances résultent d'une séquence de décisions et non d'une seule. De ce fait, il est difficile d'établir quelle est la meilleure décision à prendre à un instant donné. Plusieurs auteurs ont utilisé la simulation pour apprendre des bonnes pratiques à l'aide d'approches d'apprentissage automatique, mais se sont heurtés à la difficulté d'obtenir des exemples ou des observations sur des décisions en temps réel, où la prise en compte des changements d'états est indispensable pour choisir des stratégies de production. Nous avons réussi à aborder ce problème en proposant une approche d'apprentissage à l'aide de réseaux de neurones, qui ne nécessite pas d'exemples, d'observations ni de connaissances d'experts préalables. Ce type d'apprentissage s'effectue par optimisation via simulation des paramètres du réseau de neurones par rapport à un objectif de performance du système. Il vise à extraire de façon autonome des connaissances sur la meilleure façon de décider d'un modèle de simulation. Nous montrons la faisablité et l'apport de notre approche sur deux exemples inspirés de la littérature

In the past most of the works on structural optimisation have been based on either mathematical programming or optimality criteria methods and have mainly concentrated on static responses of structures. These optimisation methods are mathematically complex and have limited applications. A novel approach to structural optimisation is being developed for practical applications based on the concept of slowly removing the inefficient material or gradually shifting the material from the strongest part of the structure to the weakest part until the structure evolves towards the desired optimum. From the results of finite element analysis, the contribution of each element to the required structural response may be assessed. Based on this assessment, material is gradually shifted or removed in the design domain. In doing so optimum designs can be easily achieved without resorting to any complex mathematics. This optimisation procedure is called Evolutionary Structural Optimisation (ESO). This project examines the suitability of the ESO for the design of structures with buckling constraints.

This thesis studies the topology optimisation of continuum structures. Two methods have been investigated, namely. Evolutionary Structural Optimisation (ESO) and Bidirectional ESO (BESO). The basic concept of ESO is that by systematically removing inefficient materials from the structure, the residual shape evolves toward an optimum. BESO is an extension of ESO by allowing for adding efficient materials.

This thesis presents a method for structural optimisation called bi-directional evolutionary structural optimisation (BESO). It is an extension of the systematic research on the evolutionary method. The basic concept of evolutionary structural optimisation (ESO) is that by slowly removing the inefficient material, the structure evolves towards an optimum. BESO extends the concept by allowing for the efficient material to be added while the inefficient material is removed. The formulation of BESO is motivated to improve the reliability and efficiency of the ESO method. The BESO method for topological optimisation of 2D continua subject to stiffness and displacement constraints is the major task of this thesis. The theoretical aspects are explored by following the optimality criteria algorithm for problems of discrete design variables. These aspects include the optimality criteria, sensitivity analysis, displacement extrapolation and evolutionary procedure. The bi-directional evolutionary procedure is incorporated with the finite element analysis to realise an automatic optimisation process. A wide range of examples are tested by using the proposed BESO procedure. Different design conditions are considered including stiffness optimisation and single or multiple displacement optimisation under single and multiple loading conditions. The solution reliability and parametric effect are further studied to improve the BESO performance. The comparison of results by BESO and ESO are attempted and the satisfactory agreement demonstrates the validity of the proposed procedure. Two major conclusions are derived from the work in this thesis. The first one is that BESO is as effective as ESO, and the second one is that BESO can be computationally more efficient in most cases.