The design and analysis of an automotive structure is an important stage of the vehicle design process. The structural characteristics have significant impact on the vehicle performance. During the design process it is necessary to have knowledge about the structural characteristics; however in the preliminary design stages detailed information about the structure is not available. During this period of the design process the structure is often simplified to a representative model that can be analyzed and used as the input for the detailed design process. A vehicle model is developed based on the space frame structures where the frame is the load carrying portion of the structure. Preliminary design analysis is conducted using a static load condition applied to the vehicle as pure bending and pure torsion. The deflections of the vehicle based on these loading conditions are determined using the finite element method which has been implemented in developed software. The structural response, measured as the bending and torsion stiffness, is used to evaluate the structural design. An optimization program is implemented to improve the structural design with the goal of reducing weight while increasing stiffness. Following optimization the model is completed by estimating suitable plate thicknesses using a method of substructure analysis. The output of this process will be an optimized structural model with low weight and high stiffness that is ready for detailed design. / UOIT

The research objective of this dissertation is to create and demonstrate methods for parameterizing the Pareto frontiers of continuous multi-attribute design problems using barycentric coordinates, and in doing so, to enable intuitive exploration of optimal trade spaces. This work is enabled by two observations about Pareto frontiers that have not been previously addressed in the engineering design literature. First, the observation that the mapping between non-dominated designs and Pareto efficient response vectors is a bijection almost everywhere suggests that points on the Pareto frontier can be inverted to find their corresponding design variable vectors. Second, the observation that certain common classes of Pareto frontiers are topologically equivalent to simplices suggests that a barycentric coordinate system will be more useful for parameterizing the frontier than the Cartesian coordinate systems typically used to parameterize the design and objective spaces. By defining such a coordinate system, the design problem may be reformulated from y = f(x) to (y,x) = g(p) where x is a vector of design variables, y is a vector of attributes and p is a vector of barycentric coordinates. Exploration of the design problem using p as the independent variables has the following desirable properties: 1) Every vector p corresponds to a particular Pareto efficient design, and every Pareto efficient design corresponds to a particular vector p. 2) The number of p-coordinates is equal to the number of attributes regardless of the number of design variables. 3) Each attribute y_i has a corresponding coordinate p_i such that increasing the value of p_i corresponds to a motion along the Pareto frontier that improves y_i monotonically. The primary contribution of this work is the development of three methods for forming a barycentric coordinate system on the Pareto frontier, two of which are entirely original. The first method, named "non-domination level coordinates," constructs a coordinate system based on the (k-1)-attribute non-domination levels of a discretely sampled Pareto frontier. The second method is based on a modification to an existing "normal boundary intersection" multi-objective optimizer that adaptively redistributes its search basepoints in order to sample from the entire frontier uniformly. The weights associated with each basepoint can then serve as a coordinate system on the frontier. The third method, named "Pareto simplex self-organizing maps" uses a modified a self-organizing map training algorithm with a barycentric-grid node topology to iteratively conform a coordinate grid to the sampled Pareto frontier.

Im bisherigen Auslegungsprozess wird ein Bahnsystem in der Regel in Teilsysteme zerlegt, die nacheinander und für sich betrachtet entworfen werden. Das Verhalten des Gesamtsystems im geplanten täglichen Betrieb wird nur für wenige Varianten mittels Simulation überprüft. In dieser Arbeit wird der Ansatz vorgestellt, ein elektrisches Bahnsystem als Optimierungsaufgabe zu modellieren und diese mit einem geeigneten mathematischen Suchverfahren zu lösen, um Wechselwirkungen im Gesamtsystem bereits während der Dimensionierung berücksichtigen zu können. Zu diesem Zweck wird ein mehrkriterieller genetischer Algorithmus mit Zugfahrtsimulation und Netzberechnung gekoppelt, um ein für elektrische Bahnen entwickeltes Optimierungsmodell zu lösen. Am Beispiel einer realen Metrostrecke wird das Verfahren auf seine Eignung getestet und die erzielten Ergebnisse bewertet. / In the previous design process the electric railway system was subdivided into subsystems that are conceived one after the other and independent of each other. The performance of the complete railway system under realistic operation conditions can only be verified for some very few variants using simulation tools. The paper presents an approach to formulate an electric railway system as a self-contained optimization problem solved by means of a mathematical optimization method in order to consider interactions within the system in the early stage of the design process. Therefore a multi-objective genetic algorithm is coupled with both train simulation and electrical network calculation solving an optimization model specially designed for electrical railway systems. The proposed method is tested on an actual metro system. The results of this case study are presented and evaluated.

Généralement, les problèmes de conception de réseaux consistent à sélectionner les arcs et les sommets d’un graphe G de sorte que la fonction coût est optimisée et l’ensemble de contraintes impliquant les liens et les sommets dans G sont respectées. Une modification dans le critère d’optimisation et/ou dans l’ensemble de contraintes mène à une nouvelle représentation d’un problème différent. Dans cette thèse, nous nous intéressons au problème de conception d’infrastructure de réseaux maillés sans fil (WMN- Wireless Mesh Network en Anglais) où nous montrons que la conception de tels réseaux se transforme d’un problème d’optimisation standard (la fonction coût est optimisée) à un problème d’optimisation à plusieurs objectifs, pour tenir en compte de nombreux aspects, souvent contradictoires, mais néanmoins incontournables dans la réalité. Cette thèse, composée de trois volets, propose de nouveaux modèles et algorithmes pour la conception de WMNs où rien n’est connu à l’ avance. Le premiervolet est consacré à l’optimisation simultanée de deux objectifs équitablement importants : le coût et la performance du réseau en termes de débit. Trois modèles bi-objectifs qui se différent principalement par l’approche utilisée pour maximiser la performance du réseau sont proposés, résolus et comparés. Le deuxième volet traite le problème de placement de passerelles vu son impact sur la performance et l’extensibilité du réseau. La notion de contraintes de sauts (hop constraints) est introduite dans la conception du réseau pour limiter le délai de transmission. Un nouvel algorithme basé sur une approche de groupage est proposé afin de trouver les positions stratégiques des passerelles qui favorisent l’extensibilité du réseau et augmentent sa performance sans augmenter considérablement le coût total de son installation. Le dernier volet adresse le problème de fiabilité du réseau dans la présence de pannes simples. Prévoir l’installation des composants redondants lors de la phase de conception peut garantir des communications fiables, mais au détriment du coût et de la performance du réseau. Un nouvel algorithme, basé sur l’approche théorique de décomposition en oreilles afin d’installer le minimum nombre de routeurs additionnels pour tolérer les pannes simples, est développé. Afin de résoudre les modèles proposés pour des réseaux de taille réelle, un algorithme évolutionnaire (méta-heuristique), inspiré de la nature, est développé. Finalement, les méthodes et modèles proposés on été évalués par des simulations empiriques et d’événements discrets. / Generally, network design problems consist of selecting links and vertices of a graph G so that a cost function is optimized and all constraints involving links and the vertices in G are met. A change in the criterion of optimization and/or the set of constraints leads to a new representation of a different problem. In this thesis, we consider the problem of designing infrastructure Wireless Mesh Networks (WMNs) where we show that the design of such networks becomes an optimization problem with multiple objectives instead of a standard optimization problem (a cost function is optimized) to take into account many aspects, often contradictory, but nevertheless essential in the reality. This thesis, composed of three parts, introduces new models and algorithms for designing WMNs from scratch. The first part is devoted to the simultaneous optimization of two equally important objectives: cost and network performance in terms of throughput. Three bi-objective models which differ mainly by the approach used to maximize network performance are proposed, solved and compared. The second part deals with the problem of gateways placement, given its impact on network performance and scalability. The concept of hop constraints is introduced into the network design to reduce the transmission delay. A novel algorithm based on a clustering approach is also proposed to find the strategic positions of gateways that support network scalability and increase its performance without significantly increasing the cost of installation. The final section addresses the problem of reliability in the presence of single failures. Allowing the installation of redundant components in the design phase can ensure reliable communications, but at the expense of cost and network performance. A new algorithm is developed based on the theoretical approach of "ear decomposition" to install the minimum number of additional routers to tolerate single failures. In order to solve the proposed models for real-size networks, an evolutionary algorithm (meta-heuristics), inspired from nature, is developed. Finally, the proposed models and methods have been evaluated through empirical and discrete events based simulations.

This thesis is concerned with multi-objective sequential decision making (MOSDM). The motivation is twofold. On the one hand, many decision problems in the domains of e.g., robotics, scheduling or games, involve the optimization of sequences of decisions. On the other hand, many real-world applications are most naturally formulated in terms of multi-objective optimization (MOO). The proposed approach extends the well-known Monte-Carlo tree search (MCTS) framework to the MOO setting, with the goal of discovering several optimal sequences of decisions through growing a single search tree. The main challenge is to propose a new reward, able to guide the exploration of the tree although the MOO setting does not enforce a total order among solutions. The main contribution of the thesis is to propose and experimentally study two such rewards, inspired from the MOO literature and assessing a solution with respect to the archive of previous solutions (Pareto archive): the hypervolume indicator and the Pareto dominance reward. The study shows the complementarity of these two criteria. The hypervolume indicator suffers from its known computational complexity; however the proposed extension thereof provides fine-grained information about the quality of solutions with respect to the current archive. Quite the contrary, the Pareto-dominance reward is linear but it provides increasingly rare information. Proofs of principle of the approach are given on artificial problems and challenges, and confirm the merits of the approach. In particular, MOMCTS is able to discover policies lying in non-convex regions of the Pareto front, contrasting with the state of the art: existing Multi-Objective Reinforcement Learning algorithms are based on linear scalarization and thus fail to sample such non-convex regions. Finally MOMCTS honorably competes with the state of the art on the 2013 MOPTSP competition.

No description available.

The complex co-evolutionary systems approach (CCeSA) provides a well-suited framework for analysing agricultural systems, serving as a bridge between biophysical and socioeconomic sciences, allowing for the explanation of phenomena, and for the use of metaphors for thinking and action. By studying agricultural systems as self-generated, hierarchical, complex co-evolutionary farming systems (CCeFSs), one can investigate the interconnections between the elements that constitute CCeFSs, along with the relationships between CCeFSs and other systems, as a fundamental step to understanding sustainability as an emergent property of the system. CCeFSs are defined as human activity systems emerging from the purposes, gestalt, mental models, history and weltanschauung of the farm manager, and from his dynamic co-evolution with the environment while managing the resources at his hand to achieve his own multiple, conflicting, dynamic, semi-structured and constrained purposes. A sustainable CCeFS is described as one that exhibits both enough fitness to achieve its multiple, dynamic, constrained, semi-structured, and often incommensurable and conflicting purposes while performing above threshold values for failure, and enough flexibility to dynamically co-evolve with its changing biophysical and socioeconomic environment for a given future period. Fitness and flexibility are essential features of sustainable CCeFSs because they describe the systems' dynamic capacity to explore and exploit its dynamic phase space while co-evolving with it. This implies that a sustainable CCeFS is conceived as a set of dynamic, co-evolutionary processes, contrasting with the standard view of sustainability as an equilibrium or steady state. Achieving sustainable CCeFSs is a semi-structured, constrained, multi-objective, and dynamic optimisation management problem with an intractable search phase space, that can be solved within the CCeSA with the help of a multi-objective co-evolutionary optimisation tool. Carnico-ICSPEA2, a Co-Evolutionary Navigator (CoEvoNav) used as a CCeSA's tool for harnessing the complexity of the CCeFS of interest and its environment towards sustainability, is introduced. The software was designed by its end-user - the farm manager and author of this thesis - as an aid for the analysis and optimisation of the "San Francisco" ranch, a beef cattle enterprise running on temperate pastures and fodder crops in the central plateau of Mexico. By combining a non-linear simulator and a multi-objective evolutionary algorithm with a deterministic and stochastic framework, the CoEvoNav imitates the co-evolutionary pattern of the CCeFS of interest. As such, the software was used by the farm manager to "navigate" through his CCeFS's co-evolutionary phase space towards achieving sustainability at farm level. The ultimate goal was to enhance the farm manager's decision-making process and co-evolutionary skills, through an increased understanding of his system, the co-evolutionary process between his mental models, the CCeFS, and the CoEvoNav, and the continuous discovery of new, improved sets of heuristics. An overview of the methodological, theoretical and philosophical framework of the thesis is introduced. Also, a survey of the Mexican economy, its agricultural sector, and a statistical review of the Mexican beef industry are presented. Concepts such as modern agriculture, the reductionist approach to agricultural research, models, the system's environment, sustainability, conventional and sustainable agriculture, complexity, evolution, simulators, and multi-objective optimization tools are extensively reviewed. Issues concerning the impossibility of predicting the long-term, detailed future behaviour of CCeFSs, along with the use of simulators as decision support tools in the quest for sustainable CCeFSs, are discussed. The rationale behind the simulator used for this study, along with that of the multi-objective evolutionary tools used as the makeup of Carnico-ICSPEA2, are explained. A description of the "San Francisco" ranch, its key on-farm sustainability indicators in the form of objective functions, constraints, and decision variables, and the semi-structured, multi-objective, dynamic, constrained management problem posed by the farm manager's planned introduction of a herd of bulls for fattening as a way to increase the fitness of his CCeFS via a better management of the system's feed surpluses and the acquisition of a new pick-up truck are described as a case study. The tested scenario and the experimental design for the simulations are presented as well. Results from using the CoEvoNav as the farm manager's extended phenotype to solve his multi-objective optimisation problem are described, along with the implications for the management and sustainability of the CCeFS. Finally, the approach and tools developed are evaluated, and the progress made in relation to methodological, theoretical, philosophical and conceptual notions is reviewed along with some future topics for research.

La conception d'un système électrique est une tâche très complexe qui relève d’expertises dans différents domaines de compétence. Dans un contexte compétitif où l’avance technologique est un facteur déterminant, l’industrie cherche à réduire les temps d'étude et à fiabiliser les solutions trouvées par une approche méthodologique rigoureuse fournissant une solution optimale systémique.Il est alors nécessaire de construire des modèles et de mettre au point des méthodes d'optimisation compatibles avec ces préoccupations. En effet, l’optimisation unitaire de sous-systèmes sans prendre en compte les interactions ne permet pas d'obtenir un système optimal. Plus le système est complexe plus le travail est difficile et le temps de développement est important car il est difficile pour le concepteur d'appréhender le système dans toute sa globalité. Il est donc nécessaire d'intégrer la conception des composants dans une démarche systémique et globale qui prenne en compte à la fois les spécificités d’un composant et ses relations avec le système qui l’emploie.Analytical Target Cascading est une méthode d'optimisation multi niveaux de systèmes complexes. Cette approche hiérarchique consiste à décomposer un système complexe en sous-systèmes, jusqu’au niveau composant dont la conception relève d’algorithmes d'optimisation classiques. La solution optimale est alors trouvée par une technique de coordination qui assure la cohérence de tous les sous-systèmes. Une première partie est consacrée à l'optimisation de composants électriques. L'optimisation multi niveaux de systèmes complexes est étudiée dans la deuxième partie où une chaîne de traction électrique est choisie comme exemple / The design of an electrical system is a very complex task which needs experts from various fields of competence. In a competitive environment, where technological advance is a key factor, industry seeks to reduce study time and to make solutions reliable by way of a rigorous methodology providing a systemic solution.Then, it is necessary to build models and to develop optimization methods which are suitable with these concerns. Indeed, the optimization of sub-systems without taking into account the interaction does not allow to achieve an optimal system. More complex the system is more the work is difficult and the development time is important because it is difficult for the designer to understand and deal with the system in its complexity. Therefore, it is necessary to integrate the design components in a systemic and holistic approach to take into account, in the same time, the characteristics of a component and its relationship with the system it belongs to.Analytical Target Cascading is a multi-level optimization method for handling complex systems. This hierarchical approach consists on the breaking-down of a complex system into sub-systems, and component where their optimal design is ensured by way of classical optimization algorithms. The optimal solution of the system must be composed of the component's solutions. Then a coordination strategy is needed to ensure consistency of all sub-systems. First, the studied and proposed optimization algorithms are tested and compared on the optimization of electrical components. The second part focuses on the multi-level optimization of complex systems. The optimization of railway traction system is taken as a test case

Dans l’optique de refroidir les parois des chambres de combustion aéronautiques le plus efficacement possible, un intérêt particulier est aujourd’hui porté à la technologie de refroidissement par transpiration. L’air de refroidissement s’écoule au travers d’une paroi poreuse dans laquelle une grande quantité de chaleur est échangée par convection. L’éjection de l’air profite ensuite de la distribution des pores pour former une couche limite protectrice relativement homogène.Les matériaux métalliques obtenus à partir de poudres partiellement frittées sont de bons candidats pour former ces parois poreuses. Ce travail se focalise sur les échanges internes et consiste à développer une méthodologie permettant de dégager les architectures partiellement frittées les plus adaptées à ce type d’application.L’écoulement et les échanges de chaleur lors du refroidissement par transpiration sont régis par quelques propriétés effectives des matériaux qui sont fonction de l’architecture : la conductivité thermique effective, le coefficient de transfert convectif volumique et les propriétés de perméabilité. A l’aide de travaux expérimentaux ou d’études numériques sur des échantillons numérisés par tomographie aux rayons X, des relations simples entre les propriétés effectives des matériaux partiellement frittés et leurs paramètres architecturaux sont tout d’abord développées. La porosité, la surface spécifique et le type de poudre utilisé sont retenus pour prédire les paramètres effectifs.Ces relations sont finalement intégrées dans un modèle de transfert de chaleur prédisant la performance d’une solution dans les conditions de fonctionnement du moteur. Une optimisation "multi-objectifs" et une analyse des designs optimaux permettent alors de mettre en valeur quelques architectures montrant un fort potentiel pour des applications de refroidissement par transpiration. Des matériaux peu poreux formés à partir de larges poudres irrégulières semblent assurer le meilleur compromis entre tous les critères pris en compte. / In order to cool aero-engine combustion chambers as efficiently as possible, there is today a special interest given to transpiration cooling technology. The cooling air flows through a porous liner in which a large amount of heat can be exchanged by convection. The air injection could then take benefit of the pore distribution to form a more homogeneous protective boundary layer.Partially sintered metallic materials are potential candidates to form these porous liners. The present work focuses on internal heat transfers. It aims to develop a methodology capable of highlighting the most adapted partially sintered architectures to this kind of application.During transpiration cooling, flows and heat transfers are governed by some effective material properties which depends on the porous architecture: the effective solid phase thermal conductivity, the volumetric heat transfer coefficient and the permeability properties. Thanks to experimental works and numerical studies on samples digitized by X-ray tomography, simple relationships are first developed between the effective material properties of partially sintered materials and their architectural parameters. The porosity, the specific surface area and the powder type are selected to predict the effective properties.These relationships are finally integrated into a heat transfer model predicting the thermal performance of a design at working engine conditions. A multi-objective optimization and an analysis of the optimal designs highlight some architectures as being potentially interesting for transpiration cooling. Materials with a low porosity and made of large irregular powders seem to ensure the best trade-off among the different criteria taken into consideration.

Ces travaux de recherche portent sur la synthèse systématique et l’optimisation de correcteurs numériques à structure polynomiale RST par approches métaheuristiques. Les problèmes classiques de placement de pôles et de calibrage des fonctions de sensibilité de la boucle fermée RST sont formulés sous forme de problèmes d’optimisation multi-objectif sous contraintes pour lequel des algorithmes métaheuristiques de type NSGA-II, MODE, MOPSO et epsilon-MOPSO sont proposés et adaptés. Deux formulations du problème de synthèse RST ont été proposées. La première approche, formulée dans le domaine temporel, consiste à minimiser des indices de performance, de type ISE et MO, issus de la théorie de la commande optimale et liés essentiellement à la réponse indicielle du système corrigé. Ces critères sont optimisés sous des contraintes non analytiques définis par des gabarits temporels sur la dynamique de la boucle fermée. Dans la deuxième approche de synthèse RST, une formulation dans le domaine fréquentiel est retenue. La stratégie proposée consiste à définir et calculer une fonction de sensibilité de sortie désirée en satisfaisant des contraintes de robustesse de H∞. L’utilisation de parties fixes dans la fonction de sensibilité de sortie désirée assurera un placement partiel des pôles de la boucle fermée RST. L’inverse d’une telle fonction de sensibilité désirée définira le filtre de pondération H∞ associé. Un intérêt particulier est porté à l’approche d’optimisation par essaim particulière PSO pour la résolution des problèmes multi-objectif de commande reformulés. Un algorithme MOPSO à grille adaptative est proposé et puis perfectionné à base des concepts de l’epsilon-dominance. L’algorithme epsilon-MOPSO obtenu a montré, par comparaison avec les algorithmes MOPSO, NSGA-II et MODE, des performances supérieures en termes de diversité des solutions de Pareto et de rapidité en temps de convergence. Des métriques de type distance générationnelle, taux d’erreurs et espacement sont toutefois considérées pour l’analyse statistique des résultats de mise en œuvre obtenus. Une application à la commande en vitesse variable d’un moteur électrique DC est effectuée, également pour la commande en position d’un système de transmission flexible à charges variables. La mise en œuvre par simulations numériques sur les procédés considérés est également présentée dans le but de montrer la validité et l’efficacité de l’approche de commande optimale RST proposée / This research focuses on the systematic synthesis and optimization of digital RST structure based controllers thanks to global metaheuristics approaches. The classic and hard problems of closed-loop poles placement and sensitivity functions shaping of RST control are well formulated as constrained multi-objective problems to be solved with proposed metaheuristics algorithms NSGA-II, MODE, MOPSO and especially epsilon-MOPSO. Two formulations of the metaheuristics-tuned RST problem have been proposed. The first one, which is given in the time domain, deals with the minimization of several performance criteria like the Integral Square Error (ISE) and the Maximum Overshoot (MO) indices. These optimal criteria, related primarily to the step response of the controlled plant, are optimized under non-analytical constraints defined by temporal templates on the closed-loop dynamics. In the second approach, a formulation in the frequency domain is retained. The proposed strategy aims to optimize a desired output sensitivity function satisfying H∞ robustness constraints. The use of a suitable fixed part of the optimized output sensitivity function will provide partial pole placement of the closed-loop dynamics of the digital RST controller. The opposite of such desired sensitivity function will define the associated H∞ weighting filter. The Multi-Objective Particle Swarm Optimization (MOPSO) technique is particularly retained for the resolution of all formulated multi-objective RST control problems. An adaptive grid based MOPSO algorithm is firstly proposed and then improved based on the epsilon-dominance concepts. Such proposed epsilon-MOPSO algorithm, with a good diversity of the provided Pareto solutions and fast convergence time, showed a remarkable superiority compared to the standard MOPSO, NSGA-II and MODE algorithms. Performance metrics, such as generational distance, error rate and spacing, are presented for the statistical analysis of the achieved multi-optimization results. An application to the variable speed RST control of an electrical DC drive is performed, also for the RST position control of a flexible transmission plant with varying loads. Demonstrative simulations and comparisons are carried out in order to show the validity and the effectiveness of the proposed metaheuristics-based tuned RST control approach, which is formulated in the multi-objective optimization framework