Resumo: O principal objetivo deste estudo é realizar uma análise de aspectos críticos que surgem na modelagem matemática do problema de planejamento da expansão de sistemas de transmissão a longo prazo, assim como o desenvolvimento de ferramentas computacionais para a prova de novos modelos e metodologias que possam contribuir na solução do problema de planejamento de sistemas de transmissão de energia elétrica considerando as condições dos sistemas modernos de energia elétrica. Com esta metodologia, busca-se obter uma rede de transmissão mais eficiente, e com o menor custo possível, que se adapte as novas exigências produzidas pela introdução da desregulação nos sistemas elétricos. Para isto combinam-se três aspectos: rede futura livre de congestionamento, desplanificação e incerteza na geração e na demanda futura, os quais são manipuladas desde a perspectiva mono-objetivo e multiobjetivo. A possibilidade de eliminar completamente o congestionamento na rede de transmissão é analisada através da inclusão no modelo de todos os cenários de geração factíveis futuros, e não somente alguns cenários como outros estudos. Considerar uma operação sem congestionamento para o futuro está associado a grandes custos de investimento. Para atenuar este grande custo uma opção é incluir a possibilidade de desplanificação e a inclusão dos efeitos das incertezas presentes na geração e na demanda futura no problema de planejamento. O problema de planejamento de sistemas de transmissão é um problema de programação não linear inteira mista (PNLIM) quando é usado o modelo DC. Praticamente todos os algoritmos usados para resolver este problema utilizam uma sub-rotina de programação linear (PL) para resolver problemas de PL resultantes do algoritmo de solucão do problema de planejamento, os quais são denominados... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The main goal for this study is to do an analysis of the critical issues that appear in the mathematical modeling of the transmission system expansion planning problem, when long term is considered. A methodology was developed and a computational tool, to solve the transmission expansion planning in modern electrical systems. With this methodology more efficient electrical networks are obtained, at low investment costs. This is accomplished taking into account three important aspects: open access, or congestion-free planning, uncertainty in demand and generation, and de-planning. The problem is solved using mono-objective and multi-objective methodologies. For this investigation, congestion-free transmission networks should consider all the future and feasible scenarios of generation, unlike some papers, where only a few scenarios are taken in to account. This feature is associated to high investment costs. Lower costs are often obtained by the inclusion of uncertainty in future demand and future generation. The transmission system expansion planning problem is a no-linear integer-mixed programming problem (PNLIM) when the DC model is used. Practically, all the algorithms used in the solution process, for this problem, use one subroutine of linear programming (PL) for solved the PL problems that result during the solution process, in the denominated operative problem. The solution of the PL's is the part of the problem that requires the biggest computational effort, because during the solution process is necessary to solved thousands or millions of PL's, for high size problems. the PNLIM problem is solved through the combination of a meta-heuristic method and a linear programming method. The meta-heuristic method solves the denominated investment problem and the PL the denominated operational problem. The transmission planning problem considering multiples generation scenarios... (Complete abstract click electronic access below) / Orientador: Rubén Augusto Romero Lázaro / Coorientador: José Roberto Sanches Mantovani / Banca: Carlos Roberto Minussi / Banca: Sérgio Azevedo de Oliveira / Banca: Ariovaldo Verandio Garcia / Banca: Ramón Alfonso Gallego Rendón / Doutor

[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

Over the past decades, cost-effectiveness principle or cost-benefit analysis has been employed oftentimes as a typical assessment tool for the expansion of drinking water utility. With changing public awareness of the inherent linkages between climate change, population growth and economic development, the addition of global change impact in the assessment regime has altered the landscape of traditional evaluation matrix. Nowadays, urban drinking water infrastructure requires careful long-term expansion planning to reduce the risk from global change impact with respect to greenhouse gas (GHG) emissions, economic boom and recession, as well as water demand variation associated with population growth and migration. Meanwhile, accurate prediction of municipal water demand is critically important to water utility in a fast growing urban region for the purpose of drinking water system planning, design and water utility asset management. A system analysis under global change impact due to the population dynamics, water resources conservation, and environmental management policies should be carried out to search for sustainable solutions temporally and spatially with different scales under uncertainties. This study is aimed to develop an innovative, interdisciplinary, and insightful modeling framework to deal with global change issues as a whole based on a real-world drinking water infrastructure system expansion program in Manatee County, Florida. Four intertwined components within the drinking water infrastructure system planning were investigated and integrated, which consists of water demand analysis, GHG emission potential, system optimization for infrastructure expansion, and nested minimax-regret (NMMR) decision analysis under uncertainties. In the water demand analysis, a new system dynamics model was developed to reflect the intrinsic relationship between water demand and changing socioeconomic iv environment. This system dynamics model is based on a coupled modeling structure that takes the interactions among economic and social dimensions into account offering a satisfactory platform. In the evaluation of GHG emission potential, a life cycle assessment (LCA) is conducted to estimate the carbon footprint for all expansion alternatives for water supply. The result of this LCA study provides an extra dimension for decision makers to extract more effective adaptation strategies. Both water demand forecasting and GHG emission potential were deemed as the input information for system optimization when all alternatives are taken into account simultaneously. In the system optimization for infrastructure expansion, a multiobjective optimization model was formulated for providing the multitemporal optimal facility expansion strategies. With the aid of a multi-stage planning methodology over the partitioned time horizon, such a systems analysis has resulted in a full-scale screening and sequencing with respect to multiple competing objectives across a suite of management strategies. In the decision analysis under uncertainty, such a system optimization model was further developed as a unique NMMR programming model due to the uncertainties imposed by the real-world problem. The proposed NMMR algorithm was successfully applied for solving the real-world problem with a limited scale for the purpose of demonstration.

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.

La prise de décision est une partie intégrante de notre vie quotidienne où le décideur est confronté à des problèmes composés de plusieurs objectifs habituellement contradictoires. Dans ce travail, nous traitons des problèmes d'optimisation multiobjectif dans des espaces de recherche continus ou discrets. Nous avons développé plusieurs nouveaux algorithmes basés sur les métaheuristiques hybrides évolutionnaires, en particulier sur l'algorithme MOEA/D. Nous avons proposé l'algorithme HEMH qui utilise l'algorithme DM-GRASP pour construire une population initiale de solutions de bonne qualité dispersées le long de l'ensemble des solutions Pareto optimales. Les résultats expérimentaux montrent la supériorité de toutes les variantes hybrides proposées sur les algorithmes originaux MOEA/D et SPEA2. Malgré ces bons résultats, notre approche possède quelques limitations, levées dans une version améliorée de HEMH : HEMH2 et deux autres variantes HEMHde et HEMHpr. Le Adaptive Binary DE inclus dans les HEMH2 et HEMHde a de meilleures capacités d'exploration qui pallient aux capacités de recherche locale contenues dans la HEMH, HEMH2 et HEMHde. Motivés par ces résultats, nous avons proposé un nouvel algorithme baptisé HESSA pour explorer un espace continu de recherche où le processus de recherche est réalisé par différentes stratégies de recherche. Les résultats expérimentaux montrent la supériorité de HESSA à la fois sur MOEA/D et dMOPSO. Tous les algorithmes proposés ont été vérifiés, testé et comparés à certaines méthodes MOEAs. Les résultats expérimentaux montrent que toutes les propositions sont très compétitives et peuvent être considérés comme une alternative fiable / Many real-world decision making problems consist of several conflicting objectives, the solutions of which is called the Pareto-optimal set. Hybrid metaheuristics proved their efficiency in solving these problems. They tend to enhance search capabilities by incorporating different metaheuristics. Thus, we are concerned with developing new hybrid schemes by incorporating different strategies with exploiting the pros and avoiding the drawback of the original ones. First, HEMH is proposed in which the search process includes two phases DMGRASP obtains an initial set of efficient solutions in the 1st phase. Then, greedy randomized path-relinking with local search or reproduction operators explore the non-visited regions. The efficient solutions explored over the search are collected. Second, a comparative study is developed to study the hybridization of different metaheuristics with MOEA/D. The 1st proposal combines adaptive discrete differential Evolution with MOEA/D. The 2nd combines greedy path-relinking with MOEA/D. The 3rd and the 4th proposals combine both of them in MOEA/D. Third, an improved version of HEMH is presented. HEMH2 uses inverse greedy to build its initial population. Then, differential evolution and path-relink improves these solutions by investigating the non-visited regions in the search space. Also, Pareto adaptive epsilon concept controls the archiving process. Motivated by the obtained results, HESSA is proposed to solve continuous problems. It adopts a pool of search strategies, each of which has a specified success ratio. A new offspring is generated using a randomly selected one. Then, the success ratios are adapted according to the success of the generated offspring. The efficient solutions are collected to act as global guides. The proposed algorithms are verified against the state of the art MOEAs using a set of instances from literature. Results indicate that all proposals are competitive and represent viable alternatives

[pt] A programação de produção em refinaria pode ser compreendida como decisões que buscam otimizar alocação de recursos, o sequenciamento de atividades e a sua realização temporal, respeitando restrições e visando ao atendimento de múltiplos objetivos. Apesar da complexidade e natureza combinatória, a atividade carece de sistemas sofisticados que auxiliem o processo decisório, especialmente baseadas em otimização, pois as ferramentas utilizadas são planilhas ou softwares de simulação. A diversidade de objetivos do problema não implica em equivalência de importância. Pode-se considerar que existem grupos, onde os que afetam diretamente a capacidade produtiva da refinaria se sobrepõem aos associados à maior continuidade operacional. Esta tese propõe o desenvolvimento de algoritmos multiobjetivos para programação de petróleo em refinaria. As propostas se baseiam em conceituadas técnicas da literatura multiobjetivo, como dominância de Pareto e decomposição do problema, integradas à programação genética com inspiração quântica. São estudados modelos em um ou dois níveis de decisão. A diferenciação dos grupos de objetivos é avaliada com base em critérios estabelecidos para considerar uma solução proposta como aceitável e também é avaliada a influência de uma população externa no processo evolutivo. Os modelos são testados em cenários de uma refinaria real e os resultados são comparados com um modelo que trata os objetivos de forma hierarquizada. As abordagens baseadas em dominância e em decomposição apresentam vantagem sobre o algoritmo hierarquizado, e a decomposição é superior. Numa comparação com o modelo em dois níveis de decisão, apenas o que utiliza estratégia de decomposição em cada nível apresenta bons resultados. Ao final deste trabalho é obtido mais de um modelo multiobjetivo capaz de oferecer um conjunto de soluções que atendam aos objetivos críticos e deem flexibilidade de análise a posteriori para o programador de produção, o que, por exemplo, permite que ele pondere questões não mapeadas no modelo. / [en] Refinery scheduling can be understood as a set of decisions which aims to optimize resource allocation, task sequencing, and their time-related execution, respecting constraints and targeting multiple objectives. Despite its complexity and combinatorial nature, the refinery scheduling lacks more sophisticated support decision tools. The main systems in the area are worksheets and, sometimes, simulation software. The multiple objectives do not mean they have the same importance. Actually, they can be grouped whereas the objectives related to the refinery production capacity are more important than the ones related to a smooth operation. This thesis proposes the development of multiobjective algorithms applied to crude oil refinery scheduling. The proposals are based on the major technics of multiobjective literature, like Pareto dominance and problem decomposition, integrated with a quantum-inspired genetic programming approach. One and two decision level models are studied. The difference between groups is handled with conditions that define what can be considered a good solution. The effect of using an archive population in the evolutionary process is also evaluated. The results of the proposed models are compared with another model that handles the objectives in a hierarchical logical. Both decomposition and dominance approaches have better results than the hierarchical model. The decomposition model is even better. The bilevel decomposition method is the only one, among two decision levels models, which have shown good performance. In the end, this work achieves more than one multiobjective model able to offer a set of solutions which comprises the critical objectives and can give flexibility to the production scheduler does his analysis. Therefore, he can consider aspects not included in the model, like the forecast of crude oil batches not scheduled yet.

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