In this study, we propose two biobjective mathematical models based on PROMETHEE V method for project selection problems. We develop an interactive approach (ib-PROMETHEE V) including data mining techniques to solve the first proposed mathematical model. For the second model, we propose NSGA-II with constraint handling method. We also develop a Preference Based Interactive Multiobjective Genetic Algorithm (IMGA) to solve the second proposed mathematical model. We test the performance of NSGA-II with constraint handling method and IMGA on randomly generated test problems.

No description available.

Το πρόβλημα της βελτιστοποίησης ερωτημάτων πολλαπλών κριτηρίων σε βάσεις δεδομένων είναι ένα αρκετά δύσκολο και ενδιαφέρον ερευνητικά πρόβλημα, διότι χαρακτηρίζεται από αντικρουόμενες απαιτήσεις. Κάθε βήμα στην απάντηση ενός ερωτήματος μπορεί να εκτελεστεί με παραπάνω από έναν τρόπους. Για την επίλυση τέτοιου είδους ερωτημάτων έχουν προταθεί διάφοροι αλγόριθμοι, με πιο πρόσφατους τους: Mariposa, M' και Generate Partitions. Ο Mariposa και ο Μ' εφαρμόζονται στην βάση δεδομένων Mariposa, η οποία δίνει την δυνατότητα στον χρήστη να καθορίζει την επιθυμητή εξισορόπηση (tradeoff) καθυστέρησης/κόστους για κάθε ερώτημα που θέτει. Ο αλγόριθμος Mariposa ακολουθεί μία προσέγγιση απληστίας (greedy approach) προσπαθώντας σε κάθε βήμα να μεγιστοποιήσει το «κέρδος» ενώ ο Μ' χρησιμοποιεί σύνολα βέτιστων κατά Pareto λύσεων για την επιλογή του επόμενου βήματος στην θέση του κριτηρίου απληστίας. Τέλος, ο αλγόριθμος Generate Partition χρησιμοποιεί έναν διαχωρισμό του χώρου απαντήσεων χρησιμοποιώντας δομές R-trees πετυχαίνοντας πολύ καλή απόδοση. / The optimization of queries in distributed database systems is known to be subject to delicate trade-offs. For example, the Mariposa database system allows users to specify a desired delay-cost tradeoff (that is to supply a decreasing function u(d) specifying how much the user is willing to pay in order to receive the query results within time d) Mariposa divides a query graph into orizontal strides analyzes each stride, and uses a greedy heuristic to find the best plan for all strides.

The main goal of this thesis is the development of a novel methodology to generate creative solutions at functional level for design tasks without binding solution spaces with designers&rsquo / individual experiences and prejudices. For this purpose, an evolutionary methodology for the conceptual design of engineering products has been proposed. This methodology performs evaluation, combination and modification of the existing solutions repetitively to generate new solution alternatives. Therefore, initially a representation scheme, which is generic enough to cover all alternatives in solution domain, has been defined. Following that, the evolutionary operations have been defined and two evaluation metrics have been proposed. Finally, the computer implementation of the developed theory has been performed. The test-runs of developed software resulted in creative alternatives for the design task. Consequently, the evolutionary design methodology presents a systematic design approach for less experienced or inexperienced designers and establishes a base for experienced designers to conceive many other solution alternatives beyond their experiences.

No description available.

It has been increasingly recognized that realistic problems often involve a tradeoff among many conflicting objectives. Traditional methods aim at satisfying multiple objectives by combining them into a global cost function, which in most cases overlooks the underlying tradeoffs between the conflicting objectives. This raises the issue about how different objectives should be combined to yield a final solution. Moreover, such approaches promise that the chosen overall objective function is optimized over the training samples. However, there is no guarantee on the performance in terms of the individual objectives since they are not considered on an individual basis. Motivated by these shortcomings of traditional methods, the objective in this dissertation is to investigate theory, algorithms, and applications for problems with competing objectives and to understand the behavior of the proposed algorithms in light of some applications. We develop a multi-objective programming (MOP) framework for finding compromise solutions that are satisfactory for each of multiple competing performance criteria. The fundamental idea for our formulation, which we refer to as iterative constrained optimization (ICO), evolves around improving one objective while allowing the rest to degrade. This is achieved by the optimization of individual objectives with proper constraints on the remaining competing objectives. The constraint bounds are adjusted based on the objective functions obtained in the most recent iteration. An aggregated utility function is used to evaluate the acceptability of local changes in competing criteria, i.e., changes from one iteration to the next. Conflicting objectives arise in different contexts in many problems of speech and language technologies. In this dissertation, we consider two applications. The first application is language model (LM) adaptation, where a general LM is adapted to a specific application domain so that the adapted LM is as close as possible to both the general model and the application domain data. Language modeling and adaptation is used in many speech and language processing applications such as speech recognition, machine translation, part-of-speech tagging, parsing, and information retrieval. The second application is automatic language identification (LID), where the standard detection performance evaluation measures false-rejection (or miss) and false-acceptance (or false alarm) rates for a number of languages are to be simultaneously minimized. LID systems might be used as a pre-processing stage for understanding systems and for human listeners, and find applications in, for example, a hotel lobby or an international airport where one might speak to a multi-lingual voice-controlled travel information retrieval system. This dissertation is expected to provide new insights and techniques for accomplishing significant performance improvement over existing approaches in terms of the individual competing objectives. Meantime, the designer has a better control over what is achieved in terms of the individual objectives. Although many MOP approaches developed so far are formal and extensible to large number of competing objectives, their capabilities are examined only with two or three objectives. This is mainly because practical problems become significantly harder to manage when the number of objectives gets larger. We, however, illustrate the proposed framework with a larger number of objectives.

Geração e Simplificação da Base de Conhecimento de um Sistema Híbrido Fuzzy- Genético propõe uma metodologia para o desenvolvimento da base de conhecimento de sistemas fuzzy, fundamentada em técnicas de computação evolucionária. Os sistemas fuzzy evoluídos são avaliados segundo dois critérios distintos: desempenho e interpretabilidade. Uma metodologia para a análise de problemas multiobjetivo utilizando a Lógica Fuzzy foi também desenvolvida para esse fim e incorporada ao processo de avaliação dos AGs. Os sistemas fuzzy evoluídos foram avaliados através de simulações computacionais e os resultados obtidos foram comparados com os obtidos por outros métodos em diferentes tipos de aplicações. O uso da metodologia proposta demonstrou que os sistemas fuzzy evoluídos possuem um bom desempenho aliado a uma boa interpretabilidade da sua base de conhecimento, tornando viável a sua utilização no projeto de sistemas reais. / Genetic-Fuzzy Systems Generation and Simplification of a Knowledge Base proposes a methodology to develop a knowledge base for fuzzy systems through the utilization of evolutionary computational techniques. The evolved fuzzy systems are evaluated considering two distinct criteria: performance and interpretability. Another Fuzzy Logic-based methodology for multiobjective problem analysis was also developed in this work and incorporated in GAs fitness evaluation process. The aforementioned systems were analyzed through computational simulations, and the results were compared to those obtained through other methods, in some applications. The proposed methodology demonstrated that the evolved fuzzy systems are capable of not only good performance, but also good interpretation of their knowledge base, thus showing that they can be effectively used in real world projects.

La question de l’évolution du parc nucléaire français peut être considérée via l’étude de scénarios électronucléaires. Ces études présentent un rôle important, compte-tenu des enjeux, de l’ampleur des investissements, des durées et de la complexité des systèmes concernés, et fournissent des éléments d’aide au processus décisionnel. Elles sont menées à l’aide du code COSI (développé au CEA/DEN), qui permet de calculer les inventaires et les flux de matières transitant dans le cycle (réacteurs nucléaires et installations associées), via notamment le couplage avec le code d’évolution CESAR. Les études actuelles menées avec COSI nécessitent de définir les paramètres d’entrée des scénarios simulés, de sorte à satisfaire différents critères comme minimiser la consommation d’uranium naturel, la production de déchets, etc. Ces paramètres portent notamment sur les quantités et l’ordonnancement des combustibles usés au retraitement ou encore le nombre, la filière et les dates de mises en service des réacteurs à déployer. Le présent travail vise à développer, valider et appliquer une méthodologie d’optimisation couplée à COSI pour la recherche de scénarios électronucléaires optimaux pour un problème multicritère. Cette méthodologie repose en premier lieu sur la réduction de la durée d’évaluation d’un scénario afin de permettre l’utilisation de méthodes d’optimisation en un temps raisonnable. Dans ce cadre, des métamodèles d’irradiation par réseaux de neurones sont établis à l’aide de la plateforme URANIE (développée au CEA/DEN) et sont implémentés dans COSI. L’objet du travail est ensuite d’utiliser, adapter et comparer différentes méthodes d’optimisation, telles que l’algorithme génétique et l’essaim particulaire disponibles dans la plateforme URANIE, afin de définir une méthodologie adéquate pour ce sujet d’étude spécifique. La mise en place de cette méthodologie suit une approche incrémentale qui fait intervenir des ajouts successifs de critères, contraintes et variables de décision dans la définition du problème d’optimisation. Les variables ajoutées au problème, qui décrivent la cinétique de déploiement des réacteurs et la stratégie de retraitement des combustibles usés, sont choisies en fonction de leur sensibilité sur les critères définis. Cette approche permet de faciliter l’interprétation des scénarios optimaux, la détection d’éventuelles difficultés liées au processus d’optimisation, et finalement d’émettre des recommandations d’utilisation de la méthodologie mise en place en fonction de la nature du problème. Les études d'optimisation s’appuient sur un scénario de déploiement de réacteurs à neutrons rapides avec recyclage du plutonium, inspiré des études menées dans le cadre de la loi de 2006 sur la gestion des matières et déchets radioactifs. Une illustration des possibilités de la méthodologie est réalisée sur ce scénario, et permet notamment de démontrer le caractère optimal du scénario issu des études menées selon cette loi vis-à-vis de la limitation de l’entreposage de matières fissiles. Ce résultat souligne l’importance de la mise en œuvre d’une gestion dynamique du plutonium via le recours au combustible MOX pour le déploiement progressif des RNR. / The issue of the evolution French nuclear fleet can be considered through the study of nuclear transition scenarios. These studies are of paramount importance as their results can greatly affect the decision making process, given that they take into account industrial concerns, investments, time, and nuclear system complexity. Such studies can be performed with the COSI code (developed at the CEA/DEN), which enables the calculation of matter inventories and fluxes across the fuel cycle (nuclear reactors and associated facilities), especially when coupled with the CESAR depletion code. The studies today performed with COSI require the definition of the various scenarios’ input parameters, in order to fulfil different objectives such as minimising natural uranium consumption, waste production and so on. These parameters concern the quantities and the scheduling of spent fuel destined for reprocessing, and the number, the type and the commissioning dates of deployed reactors.This work aims to develop, validate and apply an optimisation methodology coupled with COSI, in order to determine optimal nuclear transition scenarios for a multi-objective platform. Firstly, this methodology is based on the acceleration of scenario evaluation, enabling the use of optimisation methods in a reasonable time-frame. With this goal in mind, artificial neural network irradiation surrogate models are created with the URANIE platform (developed at the CEA/DEN) and are implemented within COSI. The next step in this work is to use, adapt and compare different optimisation methods, such as URANIE’s genetic algorithm and particle swarm methods, in order to define a methodology suited to this type of study. This methodology development is based on an incremental approach which progressively adds objectives, constraints and decision variables to the optimisation problem definition. The variables added, which are related to reactor deployment and spent fuel reprocessing strategies, are chosen according to their sensitivity to the defined objectives. This approach makes optimal scenarios interpretation easier, makes it possible to identify potential difficulties with the optimisation process, and then to provide recommendations on the use of the deployed methodology according to the problem type. The optimisation studies consider a fast reactor deployment scenario with plutonium recycling, which is inspired by studies carried out in the scope of the 2006 Act for Waste Management. An illustration of the possibilities of this methodology is provided with this scenario, demonstrating the optimality of the scenario inspired by the studies that were carried out for the 2006 Act, regarding stored fissile materials limitation. This result highlights the importance of dynamic plutonium management through MOX fuel usage during fast reactor deployment.

Many concrete problems, ranging from Portfolio selection to Water resource management, may be cast into a multiobjective programming framework. The simplistic way of superseding blindly conflictual goals by one objective function let no chance to the model but to churn out meaningless outcomes. Hence interest of discussing ways for tackling Multiobjective Programming Problems. More than this, in many real-life situations, uncertainty and imprecision are in the state of affairs. In this dissertation we discuss ways for solving Multiobjective Programming Problems with fixed and fuzzy coefficients. No preference, a priori, a posteriori, interactive and metaheuristic methods are discussed for the deterministic case. As far as the fuzzy case is concerned, two approaches based respectively on possibility measures and on Embedding Theorem for fuzzy numbers are described. A case study is also carried out for the sake of illustration. We end up with some concluding remarks along with lines for further development, in this field. / Operations Research / M. Sc. (Operations Research)

L'évolution des architectures des systèmes embarqués temps réel vers des architectures modulaires a permis d'introduire plus de fonctionnalités grâce à l'utilisation de calculateurs répartis et d'interfaces de communication et de service standardisés. Nous nous intéressons dans cette thèse à l'architecture avionique modulaire (IMA) des standards ARINC 653 et ARINC 664 partie 7. Cette évolution a introduit de nouveaux défis de conception relatifs, entre autres, au respect des contraintes temporelles applicatives nécessaires au bon fonctionnement du système. La conception d'un système modulaire est un problème d'intégration sous contraintes, qui regroupe plusieurs problèmes difficiles (dimensionnement, allocation de ressource spatiales et temporelles). Ces difficultés requièrent la mise en place d'outils d'aide à l'intégration qui passent à l'échelle. C'est dans ce cadre-là que ces travaux de thèse ont été menés. Nous nous intéressons principalement à l'allocation des ressources temporelles du système. Plus particulièrement, nous déterminons les périodes d'exécution des fonctions embarquées distribuées qui garantissent les contraintes temporelles applicatives et qui offrent un degré d'évolutivité du système élevé, étant donné une répartition des fonctions sur les calculateurs. Notre démarche prend en compte la variabilité temporelle (bornée) du réseau de communication La première contribution de cette thèse est la formulation du problème d'intégration d'un système modulaire IMA en un problème d'optimisation multi-critère à contraintes temporelles. Pour une distribution des fonctions avioniques aux calculateurs, la périodicité des partitions IMA est recherchée de façon à garantir la fraîcheur et la non-perte des données transmises. Parmi toutes les allocations temporelles vérifiant les contraintes temporelles, nous réalisons une recherche multi-critères qui optimise à la fois un critère de charge des calculateurs et de marge temporelle dans le réseau. Ces deux critères facilitent les évolutions futures de l’architecture. La seconde contribution de cette thèse est la proposition de deux heuristiques de recherche multi-critère adaptées à notre problème. Il faut noter que le nombre d'allocations temporelles valides grandit exponentiellement avec le nombre de modules et de partitions hébergées par module. Nous proposons deux algorithmes d'optimisation multi-critères : (i) EXHAUST, un algorithme optimal de recherche exhaustive, (ii) TABOU un algorithme semi-optimal basé sur une métaheuristique Tabou. Pour les deux algorithmes, la cardinalité du problème est réduite par une phase d'optimisation locale à chaque module, rendue possible par la linéarité des deux métriques choisies. Cette première étape d'optimisation locale permet de résoudre à l'optimal le problème d'allocation avec EXHAUST pour un système IMA de taille moyenne. Nous montrons que pour des systèmes de grande taille, l'algorithme TABOU est un très bon candidat car il extrait des solutions satisfaisantes en un temps raisonnable, tout en testant un nombre limité d'allocations valides. Ces deux heuristiques sont appliquées à un système IMA. L'analyse des solutions obtenues nous permet de mettre en exergue la qualité des solutions Pareto-optimales obtenues par les deux algorithmes. Elles présentent les caractéristiques recherchées d'évolutivité de la charge des calculateurs et de la marge réseau. Notre dernière contribution réside dans une analyse fine de ces solutions. L'analyse met en avant différentes classes de solutions Pareto-optimales avec différent compromis entre la charge et la marge réseau. La connaissance de ces classes de solutions permet à l'intégrateur de choisir une solution lui fournissant le compromis qu'il recherche entre les critères de charge et de marge réseau. / The evolution of real-time embedded systems architectures to modular architectures has introduced more functionality through the use of distributed computers and communication interfaces and standardized service. We focus in this thesis on Integrated modular avionics architectures (IMA) standardized in ARINC 653 and ARINC 664 standard Part 7. This development has introduced new design challenges, among others, as respect for application timing constraints mandatory for the proper functioning of systems. The design of a modular system is an integration problem under constraints which features some difficult issues (design, spatial and temporal resource allocation). These difficulties require implementation of tools for integration that go to scale. It is, in this context, that the thesis work was conducted. We are interested primarily to the allocation of time resources of the system. In particular, we determine the execution time of distributed embedded functions that guarantee the application time constraints and offer a high degree of scalability of the system, given a distribution of functions on computers. Our approach takes into account the temporal variability (bounded variability) of the communication network. The first contribution of this thesis is the formulation of the problem of integration of an IMA system in a multi-criteria optimization problem with time constraints. For a distribution of avionics functions on computers, execution periods of IMA partitions are sought in order to ensure freshness and non-loss of transmitted data. Among all temporary allocations satisfying the time constraints, we perform a multi-criteria search that optimizes both load test calculators and time buffer in the network. These two criteria facilitate the future development of architecture. The second contribution of this thesis is the proposal of two multi-criteria search heuristics adapted to our problem. Note that the number of valid temporary allocations grows exponentially with the number of modules and partitions hosted on them. We offer two multi-criteria optimization algorithms: (i) EXHAUST, optimal exhaustive search algorithm, (ii) TABOO a semi-optimal algorithm based on a metaheuristic Tabu. For both algorithms, the cardinality of the problem is reduced by a local optimization phase for each module, made possible by the linearity of the two selected metric. This first local optimization step solves the problem of optimal allocation with EXHAUST for IMA system of medium size. We show that for large systems, the TABOO algorithm is a very good candidate because it extracts satisfactory solutions in a reasonable time while testing a limited number of valid allocations. These two heuristics are applied to an IMA system example. The analysis of the solutions obtained allows us to highlight the quality of Pareto-optimal solutions obtained by both algorithms. They have the characteristics sought scalability of the load of the computers and network margin. Our latest contribution lies in a detailed analysis of these solutions. The analysis highlights different classes of Pareto Optimal solutions with different compromise between the load of the system and the network margin. The knowledge of these solutions allows the system Integrator to choose a solution among solution classes that offer the compromise between the search criteria and network load margin.