This essay examines the strategies adopted by the Swedish parliamentary parties against the Pirate Party (Piratpartiet). The study uses the PSO-theory, which attempt to explain the success of niche parties as a consequence of the established parties’ strategies against the new competitor. A qualitative analysis and a comparison of the parties’ manifestos from the 2002, 2006 and 2010 national elections is used to determine which strategies are adopted. Results show that a mixture of accommodative and dismissive strategies is used, which could help explain the failure of the Pirate Party in the elections.

Afin d’améliorer la qualité de prédiction des techniques de classification automatique et de fouilles de données, plusieurs modèles ont été proposés dans la littérature en vue d’extraire des connaissances à partir des données. Toutefois, avec l’expansion des systèmes d’information et des technologies associées, ces techniques d’apprentissage s’avèrent de moins en moins adaptées aux nouvelles tailles et dimensions des données. On s’intéresse dans cette étude aux problèmes de grande dimensionnalité et à l’amélioration du processus d’apprentissage des méthodes de classification à travers les techniques de filtrage et de sélection d’attributs. Le problème « d’identification d’attributs pertinents » (Feature Selection Problem), tel qu’il est défini dans la littérature, relève d’une nature combinatoire. Dans le cadre de cette thèse, on s’est intéressé au développement de nouvelles techniques d’optimisation approchées et spécifiques au problème traité ainsi qu’à l’amélioration d’algorithmes existants. La conception, l’implémentation et l’étude empirique ont montré l’efficacité et la pertinence des métaheuristiques proposées. / Although the expansion of storage technologies, networking systems, and information system methodologies, the capabilities of conventional data processing techniques remain limited. The need to knowledge extraction, compact representation and data analysis are highly motivated by data expansion. Nevertheless, learning from data might be a complex task, particularly when it includes noisy, redundant and information-less attributes. Feature Selection (FS) tries to select the most relevant attributes from raw data, and hence guides the construction of final classification models or decision support systems. Selected features should be representative of the underlying data and provide effective usefulness to the targeted learning paradigm (i.e. classification). In this thesis, we investigate different optimization paradigms as well as its adaptation to the requirements of the feature selection challenges, namely the problem combinatorial nature. Both theoritical and empirical aspects were studied, and confirm the effectiveness of the adopted methodology as well as the proposed metaheuristic based approaches.

This work deals with particle swarm optimization. The theoretic part briefly describes the problem of optimization. The considerable part focuses on the overall description of particle swarm optimization (PSO). The principle, behavior, parameters, structure and modifications of PSO are described. The next part of the work is a recherché of variants of PSO, including hybridizations of PSO. In practical part the dynamic problems are analyzed and new designed algorithm for dynamic problems AHPSO is described (what it is based on, what was inspired, what elements are used and why). Algorithm is executed on the set of tasks (Moving peaks benchmark) and compared with the best publicly available variants of algorithm PSO on dynamic problems so far.

[pt] A previsão de movimentos futuros para o mercado de ações é conhecidamente uma tarefa difícil de ser satisfatoriamente realizada. Além disso, a própria possibilidade desta previsão é constantemente questionada na literatura. O estudo presente investiga se essa dificuldade poderia ser amenizada escolhendo janelas específicas de tempo, onde uma dinâmica mais evidente prevaleça, e se a identificação desses períodos pode ser aprendida através de dados passados. Um framework é proposto para tratar desses problemas. Esse framework é nomeado de Predictability Crawler (P-Craw). A proposta usa rotinas de otimização como o Particle Swarm Optimization (PSO) e Algorítimos Genéticos (GA) para selecionar sub-conjuntos de dados históricos onde modelos de aprendizado estatístico possam ser treinados de forma mais eficiente. Para validar a acurácia do método, este é testado em dois diferentes conjuntos de dados. Primeiro, simulações com diferentes níveis de ruído são geradas. Nelas, o P-Craw é capaz de identificar os subconjuntos ótimos em cenários com 20 por cento a 100 por cento de amostras previsíveis. Por fim, dados de transações intradiárias da bolsa de valores brasileira (BOVESPA) são agregados e processados uma matrix de variáveis de entrada e um vetor de previsões. Quando o P-Craw é testado contra o método usual de treinar os modelos em todo conjunto histórico disponível nos dados da BOVESPA, o framework é capaz de aumentar significativamente o número de vezes que o modelo acerta a direção do movimento do preço das ações, enquanto consegue chegar a reduzir em até 19 por cento o erro médio absoluto da tarefa. / [en] Predicting stock movements in the market its known to be an extremely difficult task. More than that, the predictability of the series itself is a controversial matter. The present study investigates if this difficulty could be alleviated by choosing specific windows of time where a more structured dynamic prevails, and whether the identification of those moments could be learned from past data. In order to do that, a novel framework is proposed. This framework is called the Predictability Crawler (P-Craw). It uses optimizations routines such as the Particle Swarm Optimization (PSO) or Genetic Algorithms (GA) to select subsets of historical data where statistical learning algorithms can be more efficiently trained. To access the accuracy of the method, it is tested against two different datasets. First, simulated data with varying percentage of noise is generated and used. In the simulations, The P-Craw is able to reliably identify the optimal subsets in scenarios ranging from 20 percent to 100 percent of predictable samples in the data. Second, intraday data from the Brazilian stocks exchange (BOVESPA) is collected and aggregated into feature and target matrices. When benchmarked against training with the whole samples in the BOVESPA data, the framework is able to significantly raise the correct directional changes of the trained models while reducing the Mean Absolute Error in up to 19 percent.

[pt] Com o objetivo de desenvolver um sistema de apoio à decisão na área de manutenção embarcada, este trabalho apresenta um modelo para problemas de ordem com restrições: CPSO(mais). Este modelo é a combinação de dois modelos da literatura, o PSO(mais), que apresenta bons resultados em problemas com restrições, e o CPSO, que introduz as modificações necessárias para aplicar o PSO em problemas de ordem. O modelo proposto foi adaptado para resolver o complexo problema de definir a melhor sequência de atividades embarcadas e funcionários alocados, de forma a maximizar o lucro da prestadora de serviço no período de três meses respeitando todas as restrições de prazo de conclusão dos serviços e restrições específicas do segmento offshore. Para avaliar o desempenho deste novo modelo na resolução do problema proposto, duas variantes do CPSO(mais) foram avaliadas frente ao modelo da literatura, CPSO, em seis casos de simulação propostos. Conclui-se pelos resultados das simulações que o modelo CPSO(mais) com inicialização reduzida destaca-se dos demais avaliados por apresentar um tempo de execução moderado e com soluções melhores que as dos demais. / [en] In order to develop an offshore maintenance support system, this work presents a model for constrained combinatorial problems: CPSO(plus). This model is a combination of two models, the PSO(plus), which presented good results in problems with constrains, and the CPSO, which is an adaptation of PSO for application in combinatorial problems. The proposed model has been adapted to solve the complex problem of defining the best sequence of offshore activities and allocated staff so as to maximize service provider profitability within three months while respecting all service completion time constraints and specific offshore work constraints. To evaluate the performance of this new model in solving the proposed problem, two CPSO(plus) variants were evaluated against the literature model, CPSO, in six proposed simulation cases. It is concluded from the results of the simulations that the CPSO(plus) model with reduced initialization outperforms other evaluated models with respect to execution time and solutions to given problem.

The purpose of this study is to examine the strategic choice of mainstream parties in relation to the competition of voters posed by a niche party and their most important issue, in this case radicalist rightwing populists and the migration issue. The study uses a comparative approach to examine the mainstream parties Social Democrats and Moderates reaction to the niche parties New Democracy 1991-1994 and Sweden Democrats 2010-2015. Using Meguid´s PSO-theory and by performing an qualitative analyse of the parties rhetoric and political suggestions in the parliamentary debates as well as in government bills and reservations in committee reports, the study aims to describe mainstream parties position on the issue and if and how they change position and strategy. The results of the study shows that both mainstream parties over all applies an adversarial strategy, aiming to maintain distance to the niche party and its position but with time and due to changes in the political environment, changes in position and strategy takes place and the mainstream parties applies a slightly more accommodative strategy.

This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly.

This thesis studies the effectiveness of bio-inspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external noise. The angular pattern depends on the number of antenna elements, their geometrical arrangement, and their relative amplitude and phases. In the present work different antenna geometries are tested and compared when their array weights are optimized by different techniques. First, the Genetic Algorithm and Particle Swarm Optimization algorithms are used to find the best set of phases between antenna elements to obtain a desired antenna pattern. This pattern must meet several restraints, for example: Maximizing the power of the main lobe at a desired direction while keeping nulls towards interferers. A series of experiments show that the PSO achieves better and more consistent radiation patterns than the GA in terms of the total area of the antenna pattern. A second set of experiments use the Signal-to-Interference-plus-Noise-Ratio as the fitness function of optimization algorithms to find the array weights that configure a rectangular array. The results suggest an advantage in performance by reducing the number of iterations taken by the PSO, thus lowering the computational cost. During the development of this thesis, it was found that the initial states and particular parameters of the optimization algorithms affected their overall outcome. The third part of this work deals with the meta-optimization of these parameters to achieve the best results independently from particular initial parameters. Four algorithms were studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Hill Climb. It was found that the meta-optimization algorithms Local Unimodal Sampling and Pattern Search performed better to set the initial parameters and obtain the best performance of the bio-inspired methods studied.

The aim of the thesis was to investigate the usage of PSO algorithm in the area of cryptanalysis. We applied PSO to the problem of simple substitution and to DES attack. By a modified version of PSO algorithm we achieved better or comparable results as by the usage of other biologically motivated algorithms. We suggested a method how to use PSO to attack DES and we were able to break it with the knowledge of only 20 plain texts and corresponding cipher texts. We have analyzed the reasons of failure to break more than a 4 rounds of DES and provided explanation for it. At the end we described the basic principles of differential cryptanalysis for DES and presented a specific mo- dification of PSO for searching optimal differential characteristics for DES. For simple ciphers, PSO is working efficiently but for sophisticated ciphers like DES, without in- corporating deep internal knowledge about the process into the algorithm, we could not expect significant outcomes. 1

This work deals with swarm intelligence, strictly speaking particle swarm intelligence. It shortly describes questions of optimization and some optimization techniques. Part of this work is recherché of variants of particle swarm optimization algorithm. These algorithms are mathematically described. Their advantages or disadvantages in comparison with the basic PSO algorithm are mentioned. The second part of this work describes mQPSO algorithm and created modification mQPSOPC. Described algorithms are compared with each other and with another evolution algorithm on several tests.