Flooding is an important concern for the UK, as evidenced by the many extreme flooding events in the last decade. Improved flood risk intervention strategies are therefore highly desirable. The application of hydroinformatics tools, and optimisation algorithms in particular, which could provide guidance towards improved intervention strategies, is hindered by the necessity of performing flood modelling in the process of evaluating solutions. Flood modelling is a computationally demanding task; reducing its impact upon the optimisation process would therefore be a significant achievement and of considerable benefit to this research area. In this thesis sophisticated multi-objective optimisation algorithms have been utilised in combination with cutting-edge flood-risk assessment models to identify least-cost and most-benefit flood risk interventions that can be made on a drainage network. Software analysis and optimisation has improved the flood risk model performance. Additionally, artificial neural networks used as feature detectors have been employed as part of a novel development of an optimisation algorithm. This has alleviated the computational time-demands caused by using extremely complex models. The results from testing indicate that the developed algorithm with feature detectors outperforms (given limited computational resources available) a base multi-objective genetic algorithm. It does so in terms of both dominated hypervolume and a modified convergence metric, at each iteration. This indicates both that a shorter run of the algorithm produces a more optimal result than a similar length run of a chosen base algorithm, and also that a full run to complete convergence takes fewer iterations (and therefore less time) with the new algorithm.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-01-15T14:16:25Z No. of bitstreams: 1 denisemanueldacostavargas.pdf: 16589539 bytes, checksum: 44a0869db27ffd5f8254f85fb69ab78c (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-01-25T17:40:31Z (GMT) No. of bitstreams: 1 denisemanueldacostavargas.pdf: 16589539 bytes, checksum: 44a0869db27ffd5f8254f85fb69ab78c (MD5) / Made available in DSpace on 2016-01-25T17:40:31Z (GMT). No. of bitstreams: 1 denisemanueldacostavargas.pdf: 16589539 bytes, checksum: 44a0869db27ffd5f8254f85fb69ab78c (MD5) Previous issue date: 2015-11-05 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Problemas de Otimização Multiobjetivo (POMs) com restrições são frequentes em diversas áreas das ciências e engenharia, entre elas a Otimização Estrutural (OE). Apesar da Evolução Diferencial (ED) ser uma metaheurística muito atraente na resolução de problemas do mundo real, há uma carência na literatura de discussões sobre o desempenho em POMs de OE. Na sua grande maioria os problemas de OE apresentam restrições. Nesta tese utiliza-se uma técnica para o tratamento de restrições chamada de APM (Adaptive Penalty Method) que tem histórico de bons resultados quando aplicada em problemas monobjetivo de OE. Pelo potencial da ED na resolução de problemas do mundo real e da técnica APM em OE, juntamente com a escassez de trabalhos envolvendo esses elementos em POMs de OE, essa tese apresenta um estudo de um algoritmo bem conhecido de ED acoplado à técnica APM nesses problemas. Experimentos computacionais considerando cenários sem e com inserção de informações de preferência do usuário foram realizados em problemas com variáveis continuas e discretas. Os resultados foram comparados aos encontrados na literatura, além dos obtidos pelo algoritmo que representa o estado da arte. Comparou-se também os resultados obtidos pelo mesmo algoritmo de ED adotado, porém sem ser acoplado à técnica APM, objetivando investigar sua influência no desempenho da combinação proposta. As vantagens e desvantagens do algoritmo proposto em cada cenário são apresentadas nessa tese, além de sugestões para trabalhos futuros. / Multiobjective Optimization Problems (MOPs) with constraints are common in many areas of science and engineering, such as Structural Optimization (SO). In spite of Differential Evolution (DE) being a very attractive metaheuristic in real-world problems, no work was found assessing its performance in SO MOPs. Most OE problems have constraints. This thesis uses the constraint handling technique called Adaptive Penalty Method (APM) that has a history of good results when applied in monobjective problems of SO. Due to the potential of DE in solving real world problems and APM in SO problems, and also with the lack of studies involving these elements in SO MOPs, this work presents a study of a well-known DE algorithm coupled to the APM technique in these problems. Computational experiments considering scenarios with and without inclusion of user preference information were performed in problems with continuous and discrete variables. The results were compared with those in the literature, in addition to those obtained by the algorithm that represents the state of the art. They were also compared to the results obtained by the same DE algorithm adopted, but without the APM technique, aiming at investigating the influence of the APM technique in their performance. The advantages and disadvantages of the proposed algorithm in each scenario are presented in this research, as well as suggestions for future works.

Constructing a road is a complex process that may be represented as a series of correlated steps, from the planning to the construction and usage of the new road. At the heart of this process, the preliminary and detailed design stages are key elements that will ensure the quality and the adequacy of the final solution regarding the constraints and objectives of the project. In particular, infrastructure layout and design will have a strong impact on the global performances of the road in operational conditions. Among them, road safety, mobility, environment preservation, noise pollution limitation, economic feasibility and viability of the project, or even its socio-economic impact at the local level. Consequently, it is crucial to offer engineers and road planners some tools and methods that may assist them in designing and selecting the most efficient solutions considering the distinctive features of each design problem. In this work, a multicriteria analysis methodology is developed to carry out an integrated and preventive assessment of road projects at the design stage by considering both their safety performances and some economic and environmental aspects. Its purpose is to support design engineers in the analysis of their projects and the identification of innovative, consistent and effective solutions. The proposed methodology is composed of two main research frameworks. On the one hand, the road design problem is addressed by focusing successively on the structuring of the multicriteria problem, the identification of the approximate set of non-dominated solutions using a genetic algorithm (based on NSGA-II), and the application of the methodology to a real road design project. On the other hand, the methodological development of a multicriteria interval clustering model was performed (based on PROMETHEE). Due to the applicability of this model to the studied problem, the interactions between the two frameworks are also analysed. / Doctorat en Sciences de l'ingénieur et technologie / The present PhD thesis is an aggregation of published contributions related to the application of multicriteria analysis to the evaluation of road projects at the design stage. The aim of the two introductory chapters is to offer a synthesised and critical presentation of the scientific contributions that constitute the PhD thesis. The complete version of the journal articles and preprints are found in Chapters 3 to 6. In the appendices, we also provide reprints of conference papers that are usually related to one of the main contributions of the thesis. / info:eu-repo/semantics/nonPublished

This thesis deals with radio network design for a chosen part of a map. Here map is represented by digital map file, which was created within the project DEM. First step is to calculate distances between points in chosen map. With help of optimization algorithms, appropriate position of transceiver in the map and parameters of radio systems are determined, also final coverage by radio signal, represented by intensity of electric field or received power in whole map. The optimization algorithm is used to find the best solution in terms of input parameters (e.g. power of transmitter, height of mast) and resulting coverage of land by radio signal.

[pt] Problemas reais de engenharia mecânica podem compreender tarefas de i) otimização multi-objetivo (MO) ou ii) regressão, classificação e predição. Os métodos baseados em inteligência artificial (AI) são bastante difundidos na resolução desses problemas por i) demandarem menor custo computacional e informações do domínio do problema para a resolução de uma MO, quando comparados com métodos de programação matemática, por exemplo; e ii) apresentarem melhores resultados com estrutura mais simples, adaptabilidade e interpretabilidade, em contraste com outros métodos. Sendo assim, o presente trabalho busca i) otimizar um controle proporcional-integral-derivativo (PID) aplicado a um sistema de frenagem anti-travamento de rodas (ABS) e o projeto de trocadores de calor de placas aletadas (PFHE) e casco-tubo (STHE) através de métodos de otimização baseados AI, buscando o desenvolvimento de novas versões dos métodos aplicados, e.g. multi-objective salp swarm algorithm (MSSA) e multi-objective heuristic Kalman algorithm (MOHKA), que melhorem a performance da otimização; ii) desenvolver um sistema de detecção de vazamento em dutos (LDS) sensível ao roubo de combustível a partir do treinamento de árvores de decisão (DTs) com features baseadas no tempo e na análise de componentes principais (PCA), ambas exraídas de dados de transiente de pressão de operação normal do duto e de roubo de combustível; iii) constituir um guia de aplicação para problemas de MO de controle e projeto, processo de extração de features e treinamento de classificadores baseados em aprendizado de máquina (MLCs), através de aprendizado supervisionado; e, por fim iv) demonstrar o potencial das técnicas baseadas em AI. / [en] Real-world mechanical engineering problems may comprise tasks of i) multi-objective optimization (MO) or ii) regression, classification and prediction. The use of artificial intelligence (AI) based methods for solving these problems are widespread for i) demanding less computational cost and problem domain information to solve the MO, when compared with mathematical programming for an example; and ii) presenting better results with simpler structure, adaptability and interpretability, in contrast to other methods. Therefore, the present work seeks to i) optimize a proportional-integral-derivative control (PID) applied to an anti-lock braking system (ABS) and the heat exchanger design of plate-fin (PFHE) and shell-tube (STHE) types through AI based optimization methods, seeking to develop new versions of the applied methods, e.g. multi-objective salp swarm algorithm (MSSA) and multi-objective heuristic Kalman algorithm (MOHKA), which enhance the optimization performance; ii) develop a pipeline leak detection system (LDS) sensitive to fuel theft by training decision trees (DTs) with features based on time and principal component analysis (PCA), both extracted from pressure transient data of regular pipeline operation and fuel theft; iii) constitute an application guide for control and design MO problems, feature extraction process and machine learning classifiers (MLCs) training through supervised learning; and, finally, iv) demonstrate the potential of AI-based techniques.

工学設計とは,複数の性能要求を満たすべく,膨大な数の設計変数の最適値を求め,自らが望む設計を選択していく,設計探査と意思決定のプロセスである. 本研究では,これら２つの主要課題に対して,多様かつ優れた解を高速に得るための「設計空間探査」と高次元かつ膨大な設計候補群から主要な設計パターンを抽出し,その特徴を定量的に分析するための「設計モード解析」の技術を開発し,その有効性を示した. / In engineering design, we try to find better solutions that satisfy many design requirements. Once the designs have been found, we choose preferable one. Engineering design is the mixed procedure of design exploration and decision making. This study proposes the effective solution for each engineering process: (1) a novel optimization algorithm to rapidly derive better designs, and (2) "design mode analysis" which enables us to extract representative design patterns from the huge number of design examples. Effectiveness of the proposed method were verified in real-world problems. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

Due to the high cost of manual content creation within the game development industry, methods for procedural generation of content such as game maps and levels have emerged. However, methods for generating game maps have remained relatively unexplored in competitive multiplayer contexts. Presumably, this is due to the opposing goals of generating game maps that are both interesting and fair. This study aims to explore the possibility of satisfying both these goals simultaneously by separating the generative phase from the phase that enforces fairness. In this endeavor, simple game maps for a generic multiplayer grand strategy game are generated using noise-based methods. The task of partitioning the game map fairly between the players is then modeled as a constrained categorical multiobjective minimization problem that is subsequently solved by two genetic algorithms, the reference-point-based algorithm NSGA-III and the decomposition-based algorithm MOEA/D-IEpsilon. In a primary study, the proposed partitioning method is evaluated based on the quality of the solutions produced, its scalability, and its ability to find symmetrical partitions of symmetrical game maps. The results show that the proposed method makes significant improvement from the initial guess but fails to produce completely fair partitions in general. Explanations and possible solutions to this are presented. The timing results indicate that the proposed method is not applicable in real-time contexts. However, the proposed method might still be applicable in online contexts if smaller game maps are considered and in offline contexts if larger game maps are considered. Finally, the partitioning results show that the proposed method successfully finds fair partitions of symmetrical game maps but fails to find the obvious symmetrical partitions. In a secondary study, the two genetic algorithms are compared to determine which algorithm produces dominating solutions and which algorithm produces most diverse solution. The results indicate that, for the partitioning problems considered in this study, the reference-point-based algorithm is both dominant and produces the most diverse solutions.

Ce travail a eu pour objectif de déterminer les conditions optimales de production d’un caroténoïde original, la torularhodine, par Sporobolomyces ruberrimus, cultivée en réacteur discontinu. Cette souche est capable d’utiliser le glycérol technique comme source de carbone et d’énergie pour sa croissance et pour la production de caroténoïdes. D’abord, il s’est agi d’identifier les facteurs opératoires majeurs qui sont susceptibles d’avoir une influence sur la production de la torularhodine, au travers d’une étude préliminaire. L’identification expérimentale de ces facteurs d’action - la température, le taux d’oxygène dissous et la supplémentation en acide oléique - a été validée statistiquement, à des degrés divers, avant d’engager une étape d’optimisation par la construction d’un plan d’expériences multicritère. Celui-ci a conduit à l’établissement de modèles polynômiaux du second degré pour représenter l’effet conjugué des facteurs retenus et permettre la prédiction des valeurs de µmax et de concentration de torularhodine rapportée à la biomasse. Cette étude a alors été consacrée à un essai de fonctionnalisation de la torularhodine, à partir de sa fonction carboxylique, en vue de la stabilisation de la molécule dont l’activité antioxydante est élevée. L’acylation enzymatique de la lysine par la torularhodine a été envisagée. Les conditions d’acylation par la lipase B de C. antarctica ont été déterminées avec un caroténoïde modèle, la bixine. Le produit dérivé obtenu après transacylation a été purifié et a montré une activité antiradicalaire supérieure à celle de la bixine. Ces résultats permettent d’envisager la synthèse de peptides acylés avec ce type de caroténoïdes / The aim of this work was to determine the optimum of an original carotenoid, the torularhodin, produced by Sporobolomyces ruberrimus, in batch culture. A very interesting characteristic of this strain is its ability to consume raw glycerol as a carbon and energy source for microbial growth and carotenoid production. In the fist part of this study, the identification of operating parameters that have an influence on the optimum torularhodin production, was achieved. Experimental assays reinforced by a statistical study allowed to identify temperature, dissolved oxygen pressure and oleic acid supplementation, as the major parameters of influence, and then the integration of these data was performed for the construction of a multiobjective optimization based on a multicriteria experimental design. The establishment of a mathematical model of a second degree polynomial type was developed for the prediction of the values of µmax and of the torularhodin concentration reported to biomass. In the last part, considering that torularhodin has an important antioxidant property and it exhibits a free carboxyl acid function which can be used as acyl agent, a study of its structure modifying by an enzymatic way as a stabilization pattern was started. The experimental conditions of lysine acylation by the lipase B of Candida antarctica were determined using a model carotenoid, the bixin. The resulting product of the synthesis of bixin derivative was purified and showed an antiradical activity of 2.5 times higher than that of bixin. This result showed the ability of the acylation reaction of peptides with this kind of carotenoids

Cette thèse traite de l'optimisation sous incertitude de fonctions coûteuses dans le cadre de la conception de systèmes aéronautiques.Nous développons dans un premier temps une stratégie d'optimisation robuste multiobjectifs par modèles de substitution. Au-delà de fournir une représentation plus rapide des fonctions initiales, ces modèles facilitent le calcul de la robustesse des solutions par rapport aux incertitudes du problème. L'erreur de modélisation est maîtrisée grâce à une approche originale d'enrichissement de plan d'expériences qui permet d'améliorer conjointement plusieurs modèles au niveau des régions de l'espace possiblement optimales.Elle est appliquée à la minimisation des émissions polluantes d'une chambre de combustion de turbomachine dont les injecteurs peuvent s'obstruer de façon imprévisible.Nous présentons ensuite une méthode heuristique dédiée à l'optimisation robuste multidisciplinaire. Elle repose sur une gestion locale de la robustesse au sein des disciplines exposées à des paramètres incertains, afin d'éviter la mise en place d'une propagation d'incertitudes complète à travers le système. Un critère d'applicabilité est proposé pour vérifier a posteriori le bien-fondé de cette approche à partir de données récoltées lors de l'optimisation. La méthode est mise en œuvre sur un cas de conception avion où la surface de l'empennage vertical n'est pas connue avec précision. / This PhD thesis deals with the optimization under uncertainty of expensive functions in the context of aeronautical systems design.First, we develop a multiobjective robust optimization strategy based on surrogate models.Beyond providing a faster representation of the initial functions, these models facilitate the computation of the solutions' robustness with respect to the problem uncertainties. The modeling error is controlled through a new design of experiments enrichment approach that allows improving several models concurrently in the possibly optimal regions of the search space. This strategy is applied to the pollutant emission minimization of a turbomachine combustion chamber whose injectors can clog unpredictably. We subsequently present a heuristic method dedicated to multidisciplinary robust optimization. It relies on local robustness management within disciplines exposed to uncertain parameters, in order to avoid the implementation of a full uncertainty propagation through the system. An applicability criterion is proposed to check the validity of this approach a posteriori using data collected during the optimization. This methodology is applied to an aircraft design case where the surface of the vertical tail is not known accurately.

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