Session ID: TH-E1-4 / SCIS & ISIS 2010, Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. December 8-12, 2010, Okayama Convention Center, Okayama, Japan

In view of the problem of uneven load distribution and energy consumption among nodes in a multi-hop wireless sensor network, this research constructs the routing schedule problem as a MOP (Multi-objective Optimization Problem), and proposed an energy-aware routing optimization scheme RDSEGA based on multi-objective optimization. In this scheme, in order to avoid the searching space explosion problem caused by the increase of nodes, KSP Yen's algorithm was applied to prune the searching space, and the candidate paths selected after pruning are recoded based on priority. Then adopted the improved strengthen elitist genetic algorithm to get the entire network routing optimization scheme with the best energy efficiency. At the same time, in view of the problem of routing discontinuity in the process of path crossover and mutation, new crossover and mutation method was proposed that based on the gene fragments connected by the adjacent node or the same node to maximize the effectiveness of the evolution result. The experimental results prove that the scheme reduced the energy consumption of nodes in the network, the load between nodes becomes more balanced, and the working time of the network has been prolonged nearly 40% after the optimization. This brings convenience to practical applications, especially for those that are inconvenient to replace nodes.

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Orientador: Francisco de Assis Magalhães Gomes Neto / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica / Made available in DSpace on 2018-08-25T19:03:25Z (GMT). No. of bitstreams: 1 Villela_PedroFerraz_D.pdf: 10794575 bytes, checksum: 746b8aebf0db423d557d9c5fe1446592 (MD5) Previous issue date: 2014 / Resumo: Neste trabalho, propomos um método exato para obter o conjunto solução de um problema biobjetivo quadrático de otimização de carteiras de investimento, que envolve variáveis binárias. Nosso algoritmo é baseado na junção de três algoritmos específicos. O primeiro encontra uma curva associada ao conjunto solução de problemas biobjetivo contínuos por meio de um método de restrições ativas, o segundo encontra o ótimo de um problema de programação quadrática inteira mista pelo método Branch-and-Bound, e o terceiro encontra a interseção de duas curvas associadas a problemas biobjetivo distintos. Ao longo do texto, algumas heurísticas e métodos adicionais também são introduzidos, com o propósito de acelerar a convergência do algoritmo proposto. Além disso, o nosso método pode ser visto como uma nova contribuição na área, pois ele determina, de forma exata, a curva associada ao conjunto solução do problemas biobjetivo inteiro misto, algo que é incomum na literatura, pois o problema alvo geralmente é abordado via métodos meta-heurísticos. Ademais, ele mostrou ser eficiente do ponto de vista do tempo computacional, pois encontra o conjunto solução do problema em poucos segundos / Abstract: In this work, we propose an exact method to find the solution set of a mixed quadratic bi-objective portfolio optimization problem. Our method is based on the combination of three specific algorithms. The first one obtains a curve associated with the solution set of a continuous bi-objective problem through an active set algorithm, the second one solves a mixed quadratic optimization problem through the Branch-and-Bound method, and the third one searches the intersection of two curves associated with distinct bi-objective problems. Throughout the text, some heuristics are also introduced in order to accelerate the performance of the method. Moreover, our method can be seen as a new contribution to the field, since it finds, in an exact way, the curve related to the solution set of the mixed integer bi-objective problem, something uncommon in the corresponding literature, where the target problem is usually approached by metaheuristic methods. Additionally, it has also shown to be efficient in terms of running time, being capable of finding the problem's solution set within a much faster time frame / Doutorado / Matematica Aplicada / Doutor em Matemática Aplicada

Since the term ESG was coined in 2005, the growth of sustainable investments has outpaced the overall asset management industry. A lot of research has been done with regards to the link between sustainability and financial performance, despite the fact that there is a lack of transparency in sustainability of listed companies. This thesis breaks down the word sustainability into two di↵erent categories, and in turn eleven di↵erent parameters. The result is the term Q score which represents a company’s sustainability. The purpose is to increase transparency in the fund selection process for asset managers. Further, a multiobjective optimization problem is solved to analyze the relationships between return, risk and sustainability. The main subject is that accommodating sustainability as a third parameter in addition to return and risk modifies the fund selection process. The result indicates that the relationships between sustainability, return and risk follow the ecient market hypothesis, implying that an investor would have to sacrifice risk and return in order to achieve higher sustainability. With that said, the results indicated that the sacrifice is relatively small, and that there are a number of sustainable portfolios that perform well. Moving on, the reporting of ESG company data is still lacking. For this reason, this master thesis acts as a precursor for any future development within the field. / Sedan termen ESG utvecklades år 2005, har tillväxten av hållbara investeringar vuxit snabbare än den generella förvaltningsindustrin. Mycket forskning har gjorts kring hållbarhet kopplat till finansiell avkastning, men trots detta saknas det fortfarande en transparens rådande hållbarhet av noterade bolag. Detta examensarbete bryter ned termen hållbarhet till två kategorier, vilket i sin tur bryts ner till elva kvantifierbara parametrar. Resultatet blir ett så kallat Q score, som är ett värde på ett företags hållbarhet. Syftet med arbetet är att öka transparensen av fonders hållbarhetsarbete. Vidare löses ett optimeringsproblem med tre parametrar för att undersöka förållandena mellan avkastning, risk och hållbarhet. Resultatet indikerar att dessa förhållanden följer hypotesen om effektiva marknader, vilket innebär att en investerare måste offra avkastning och risk för att uppnå en mer hållbar portfölj. Med det sagt, indikererar resultatet att en investerare inte behöver offra mycket inom avkastning för att uppnå en hållbar portfölj. Vidare kvarstår det mycket arbete inom rapporteringen av ESG data på företagsnivå. Av detta skäl anses detta examensarbete vara en föregångare innan datan utvecklas vidare.

L’ingénierie dirigée par les modèles (EDM) est un paradigme de développement logiciel qui promeut l’utilisation de modèles en tant qu’artefacts de première plan et de processus automatisés pour en dériver d’autres artefacts tels que le code, la documentation et les cas de test. La transformation de modèle est un élément important de l’EDM puisqu’elle permet de manipuler les représentations abstraites que sont les modèles. Les transformations de modèles, comme d’autres programmes, sont sujettes à la fois à des erreurs syntaxiques et sémantiques. La correction de ces erreurs est difficile et chronophage, car les transformations dépendent du langage de transformation comme ATL et des langages de modélisation dans lesquels sont exprimés les modèles en entrée et en sortie. Les travaux existants sur la réparation des transformations ciblent les erreurs syntaxiques ou sémantiques, une erreur à la fois, et définissent manuellement des patrons de correctifs. L’objectif principal de notre recherche est de proposer un cadre générique pour corriger automatiquement de multiples erreurs syntaxiques et sémantiques. Afin d’atteindre cet objectif, nous reformulons la réparation des transformations de modèles comme un problème d’optimisation multiobjectif et le résolvons au moyen d’algorithmes évolutionnaires. Pour adapter le cadre aux deux catégories d’erreurs, nous utilisons différents types d’objectifs et des stratégies sophistiquées pour guider l’exploration de l’espace des solutions. / Model-driven engineering (MDE) is a software development paradigm that promotes the use of models as first-class artifacts and automated processes to derive other artefacts from them such as code, documentation and test cases. Model transformation is an important element of MDE since it allows to manipulate the abstract representations that are models. Model transformations, as other programs are subjects to both syntactic and semantic errors. Fixing those errors is difficult and time consuming as the transformations depend on the transformation language such as ATL, and modeling languages in which input and output models are expressed. Existing work on transformation repair targets either syntactic or semantic errors, one error at a time, and define patch templates manually. The main goal of our research is to propose a generic framework to fix multiple syntactic and semantic errors automatically. In order to achieve this goal, we reformulate the repair of model transformations as a multi-objective optimization problem and solve it by means of evolutionary algorithms. To adapt the framework to the two categories of errors, we use different types of objectives and sophisticated strategies to guide the search.