In this Master's thesis scripting interface of two programs CST Microwave studio and Ansoft HFSS for the purpose of analysis of electromagnetic structures is described. The work is focuses control of these programs with help of scripting languages and system's interface of MS Windows XP. Next the process of connecting programs with MATLAB is shown on commented scripts together with an example of complete analysis of a chosen problem, and the import and export of results results in MATLAB. Further the functions which form programming interface between MATLAB and simulation programs are designed and implemented. The interconnection layer makes the complete control of simulating programs possible using the function description published in the official documentation of used simulation programs. The layer is described in reference manual in detail and it is used for optimization with use of Particle swarm optimalization (PSO) of planar antenna model. Then there is presented another usage of the layer for an implementation of global optimization methods - SOMA and DE including suggestion of process for comparison efficiency of optimization algorithms on simple electromagnetic models.

The widespread popularity of Federated Learning (FL) has led researchers to delve into its various facets, primarily focusing on personalization, fair resource allocation, privacy, and global optimization, with less attention puts towards the crucial aspect of ensuring efficient and cost-optimized communication between the FL server and its agents. A major challenge in achieving successful model training and inference on distributed edge devices lies in optimizing communication costs amid resource constraints, such as limited bandwidth, and selecting efficient agents. In resource-limited FL scenarios, where agents often rely on unstable networks, the transmission of large model weights can substantially degrade model accuracy and increase communication latency between the FL server and agents. Addressing this challenge, we propose a novel strategy that integrates a knowledge distillation technique with a Particle Swarm Optimization (PSO)-based FL method. This approach focuses on transmitting model scores instead of weights, significantly reducing communication overhead and enhancing model accuracy in unstable environments. Our method, with potential applications in smart city services and industrial IoT, marks a significant step forward in reducing network communication costs and mitigating accuracy loss, thereby optimizing the communication efficiency between the FL server and its agents.

Denna uppsats handlar om hur några av de redan etablerade partierna har bemött Feministiskt Initiativ och dess inträde i politiken och partiernas syn på jämställdhet och feminism.Vi har använt oss av kvalitativa metoder i form av intervjuer och datainsamling. Vi har intervjuat partier angående deras ideologi och bemötande samt vilka strategier de har antagit för att bemöta nischpartiet Feministiskt Initiativ. Vi har tittat närmare på Position, salience and ownership theory, PSO-teorin, för att se om partier har använt sig utav de strategier som nämns i teorin. Vi har även studerat hur tillkomsten av Feministiskt Initiativ har påverkat de etablerade partiernas prioriteringar och profilering i frågor om jämställdhet och feminism. Vi fokuserar också på tidigare forskning gällande feminismen.Partier ser annorlunda på feminism och på jämställdheten. Efter att ha intervjuat de utvalda partierna så syns det tydliga kopplingar till PSO-teorin. Vi har också studerat om partierna har satt feminism och jämställdhet högre upp på den politiska agendan sedan Feministiskt Initiativs intåg i politiken. / This essay is about how some of the already established parties have responded to the Feminist Initiative and it’s entry into politics and the parties' views on gender equality and feminism.We have used qualitative methods in the form of interviews and data collection. We interviewed the parties regarding their ideology and attitude as well as the strategies they have adopted to address niche party Feminist Initiative. We have looked at Position, salience and ownership, PSO-theory to see if the parties have used out the strategies significantly in theory. We also studied how the advent of the Feminist Initiative has affected the established parties' priorities and profiling the issues of gender equality and feminism. We also focus on earlier research on feminism.Parties look different on feminism and gender equality. After interviewing the desired parties it will show clear links with PSO-theory. We also studied whether the parties have put feminism and gender equality higher up on the political agenda since the Feminist Initiative's entry into politics.The original text is in Swedish.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2018-11-29T10:40:19Z No. of bitstreams: 2 Dissertação - Leonardo Alves Moreira de Melo - 2018.pdf: 2693689 bytes, checksum: 850fbad5a82099825d2478ba3415dcac (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-11-29T11:09:58Z (GMT) No. of bitstreams: 2 Dissertação - Leonardo Alves Moreira de Melo - 2018.pdf: 2693689 bytes, checksum: 850fbad5a82099825d2478ba3415dcac (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-11-29T11:09:58Z (GMT). No. of bitstreams: 2 Dissertação - Leonardo Alves Moreira de Melo - 2018.pdf: 2693689 bytes, checksum: 850fbad5a82099825d2478ba3415dcac (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-10-26 / Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG / In order to address an issue concerning the increasing number of algorithms based on particle swarm optimization (PSO) applied to solve large-scale optimization problems (up to 2000 variables), this article presents analysis and comparisons among five state- of-the-art PSO algorithms (CCPSO2, LSS- PSO, OBL-PSO, SPSO and VCPSO). Tests were performed to illustrate the e ciency and feasibility of using the algorithms for this type of problem. Six benchmark functions most commonly used in the literature (Ackley 1, Griewank, Rastrigin, Rosenbrock, Schwefel 1.2 and Sphere) were tested. The experiments were performed using a high-dimensional problem (500 variables), varying the number of particles (50, 100 and 200 particles) in each algorithm, thus increasing the computational complexity. The analysis showed that the CCPSO2 and OBL-PSO algorithms found significantly better solutions than the other algorithms for more complex multimodal problems (which most resemble realworld problems). However, considering unimodal functions, the CCPSO2 algorithm stood out before the others. Our results and experimental analysis suggest that CCPSO2 and OBL- PSO seem to be highly competitive optimization algorithms to solve complex and multimodal optimization problems. / O número de algoritmos baseados na otimização por enxame de partículas (PSO) aplicados para resolver problemas de otimização em grande escala (até 2.000 variáveis) aumentou significativamente. Este trabalho apresenta análises e comparações entre cinco algoritmos (CCPSO2, LSSPSO, OBL-CPSO, SPSO e VCPSO). Testes foram realizados para ilustrar a eficiência e viabilidade de usar os algoritmos para resolver problemas em larga escala. Seis funções de referência que são comumente utilizadas na literatura (Ackley 1, Griewank, Rastrigin, Rosenbrock, Schwefel 1.2 e Sphere) foram utilizadas para testar a performancedesses algoritmos. Os experimentos foram realizados utilizando um problema de alta dimensionalidade (500 variáveis), variando o número de partículas (50, 100 e 200 partículas) em cada algoritmo, aumentando assim a complexidade computacional. A análise mostrou que os algoritmos CCPSO2 e OBL-CPSO mostraram-se significativamente melhores que os outros algoritmos para problemas multimodais mais complexos (que mais se assemelham a problemas reais). No entanto, considerando as funções unimodais, o algoritmo CCPSO2 destacou-se perante os demais. Nossos resultados e análises experimentais sugerem que o CCPSO2 e o OBL-CPSO são algoritmos de otimização altamente competitivos para resolver problemas de otimização complexos e multimodais em larga escala.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-05-23T12:23:58Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) SOME NEW FAMILIES OF CONTINUOUS DISTRIBUTIONS.pdf: 5612905 bytes, checksum: 3fd32464f68606705a4b23070897a8e2 (MD5) / Made available in DSpace on 2017-05-23T12:23:58Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) SOME NEW FAMILIES OF CONTINUOUS DISTRIBUTIONS.pdf: 5612905 bytes, checksum: 3fd32464f68606705a4b23070897a8e2 (MD5) Previous issue date: 2016-06-27 / FACEPE / The area of survival analysis is important in Statistics and it is commonly applied in biological sciences, engineering, social sciences, among others. Typically, the time of life or failure can have different interpretations depending on the area of application. For example, the lifetime may mean the life itself of a person, the operating time of equipment until its failure, the time of survival of a patient with a severe disease from the diagnosis, the duration of a social event as a marriage, among other meanings. The time of life or survival time is a positive continuous random variable, which can have constant, monotonic increasing, monotonic decreasing or non-monotonic (for example, in the form of a U) hazard function. In the last decades, several families of probabilistic models have been proposed. These models can be constructed based on some transformation of a parent distribution, commonly already known in the literature. A given linear combination or mixture of G models usually defines a class of probabilistic models having G as a special case. This thesis is composed of independent chapters. The first and last chapters are short chapters that include the introduction and conclusions of the study developed. Two families of distributions, namely the exponentiated logarithmic generated (ELG) class and the geometric Nadarajah-Haghighi (NHG) class are studied. The last one is a composition of the Nadarajah-Haghighi and geometric distributions. Further, we develop a statistical library for the R programming language called the AdequacyModel. This is an improvement of the package that was available on CRAN (Comprehensive R Archive Network) and it is currently in version 2.0.0. The two main functions of the library are the goodness.fit and pso functions. The first function allows to obtain the maximum likelihood estimates (MLEs) of the model parameters and some goodness-of-fit of the fitted probabilistic models. It is possible to choose the method of optimization for maximizing the log-likelihood function. The second function presents the method meta-heuristics global search known as particle swarm optimization (PSO) proposed by Eberhart and Kennedy (1995). Such methodology can be used for obtaining the MLEs necessary for the calculation of some measures of adequacy of the probabilistic models. / A área de análise de sobrevivência é importante na Estatística e é comumente aplicada às ciências biológicas, engenharias, ciências sociais, entre outras. Tipicamente, o tempo de vida ou falha pode ter diferentes interpretações dependendo da área de aplicação. Por exemplo, o tempo de vida pode significar a própria vida de uma pessoa, o tempo de funcionamento de um equipamento até sua falha, o tempo de sobrevivência de um paciente com uma doença grave desde o diagnóstico, a duração de um evento social como um casamento, entre outros significados. O tempo de vida é uma variável aleatória não negativa, que pode ter a função de risco na forma constante, monótona crescente, monótona decrescente ou não monótona (por exemplo, em forma de U). Nas últimas décadas, várias famílias de modelos probabilísticos têm sido propostas. Esses modelos podem ser construídos com base em alguma transformação de uma distribuição padrão, geralmente já conhecida na literatura. Uma dada combinação linear ou mistura de modelos G normalmente define uma classe de modelos probabilísticos tendo G como caso especial. Esta tese é composta de capítulos independentes. O primeiro e último são curtos capítulos que incluem a introdução e as conclusões do estudo desenvolvido. Duas famílias de distribuições, denominadas de classe “exponentiated logarithmic generated” (ELG) e a classe “geometric Nadarajah-Haghighi” (NHG) s˜ao estudadas. A ´ultima ´e uma composi¸c˜ao das distribuições de Nadarajah-Haghighi e geométrica. Além disso, desenvolvemos uma biblioteca estatística para a linguagem de programação R chamada AdequacyModel. Esta é uma melhoria do pacote que foi disponibilizado no CRAN (Comprehensive R Archive Network) e está atualmente na versão 2.0.0. As duas principais funções da biblioteca são as funções goodness.fit e pso. A primeira função permite obter as estimativas de máxima verossimilhança (EMVs) dos parâmetros de um modelo e algumas medidas de bondade de ajuste dos modelos probabilísticos ajustados. E possível escolher o método de otimização para maximizar a função de log-verossimilhan¸ca. A segunda função apresenta o método meta-heurístico de busca global conhecido como Particle Swarm Optimization (PSO) proposto por Eberhart e Kennedy (1995). Algumas metodologias podem ser utilizadas para obtenção das EMVs necessárias para o cálculo de algumas medidas de adequação dos modelos probablísticos ajustados.

The thesis deals with a parametric analysis of a magnetic multiband antenna and explains the principle of its operation. In the thesis, an optimization of the antenna by the particle swarm optimization is performed in order to meet impedance matching in prescribed frequency bands.

Cette thèse porte sur l’étude et l’optimisation aérothermique d’un alterno-démarreur utilisé dans les véhicules hybrides. Ces machines produisant beaucoup plus de puissance qu’un alternateur classique, leur refroidissement est donc critique. La machine est modélisée en utilisant la méthode nodale en régime permanent qui utilise des réseaux de conductances thermiques. Pour alimenter le modèle, on utilise des corrélations issues de la littérature pour modéliser les transferts convectifs et on effectue des calculs CFD de la machine complète pour obtenir la répartition des débits. Les résultats obtenus numériquement sont ensuite validés expérimentalement à l’aide d’essais par Vélocimétrie par Images de Particules et d’essais thermiques par mesure thermocouples. Dans un deuxième temps, on couple un algorithme d’optimisation au code pour obtenir une géométrie de la machine optimale d’un point de vue thermique. La méthode retenue est l’Optimisation par Essaim Particulaire (PSO). L’optimisation se fait sur la taille des têtes de bobines, la position des ventilateurs et la section des canaux rotoriques. On obtient des géométries différentes selon les objectifs que l’on cherche à atteindre. La dernière partie de la thèse porte sur l’optimisation multi-objectifs d’un dissipateur située sur la partie électronique à l’arrière de l’alternateur : le dissipateur doit refroidir le plus possible l’électronique sans pour autant perturber l’écoulement. On étudie aussi plusieurs formes d’ailettes pour atteindre ces objectifs. / The goal of this thesis is the aero-thermal study and optimization of a starter-alternator used in hybrid cars. This kind of machines being more powerful than a regular alternator, their cooling is critical. The machine is modeled using lumped method in steady state which uses networks of thermal conductances. The inputs for the model are obtained using correlations from bibliography for the convective heat transfers and three dimensional CFD for the flow rates inside the machine. The numerical results are validated by experimental results with PIV for the fluid results and a machine fitted with thermocouples for the thermal part. In the second part, the thermal code is coupled with an optimization algorithm to obtain an optimal geometry of the machine from a thermal point of view. The method chosen is Particle Swarm Optimization (PSO). The parameters are the sizes of the end-windings, the positions of the fans and the cross section of the rotor channels. For different objectives, different optimal geometries are obtained. The last part of this work aims at the multi-objectives optimization of a heat sink located at the back of the machine. The heat sink has to be thermally efficient but should not affect the flow. Different shapes of fins are also studied.

This research provides a robust solution based on artificial intelligence (AI) techniques to overcome the challenges in Software Defined Networks (SDNs) that can jeopardise the overall performance of the network. The proposed approach, presented in the form of an intelligent agent appended to the SDN network, comprises of a new hybrid intelligent mechanism that optimises the performance of SDN based on heuristic optimisation methods under an Artificial Neural Network (ANN) paradigm. Evolutionary optimisation techniques, including Particle Swarm Optimisation (PSO) and Genetic Algorithms (GAs) are deployed to find the best set of inputs that give the maximum performance of an SDN-based network. The ANN model is trained and applied as a predictor of SDN behaviour according to effective traffic parameters. The parameters that were used in this study include round-trip time and throughput, which were obtained from the flow table rules of each switch. A POX controller and OpenFlow switches, which characterise the behaviour of an SDN, have been modelled with three different topologies. Generalisation of the prediction model has been tested with new raw data that were unseen in the training stage. The simulation results show a reasonably good performance of the network in terms of obtaining a Mean Square Error (MSE) that is less than 10−6 [superscript]. Following the attainment of the predicted ANN model, utilisation with PSO and GA optimisers was conducted to achieve the best performance of the SDN-based network. The PSO approach combined with the predicted SDN model was identified as being comparatively better than the GA approach in terms of their performance indices and computational efficiency. Overall, this research demonstrates that building an intelligent agent will enhance the overall performance of the SDN network. Three different SDN topologies have been implemented to study the impact of the proposed approach with the findings demonstrating a reduction in the packets dropped ratio (PDR) by 28-31%. Moreover, the packets sent to the SDN controller were also reduced by 35-36%, depending on the generated traffic. The developed approach minimised the round-trip time (RTT) by 23% and enhanced the throughput by 10%. Finally, in the event where SDN controller fails, the optimised intelligent agent can immediately take over and control of the entire network.

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.

Η εργασία πραγματέυεται τη δημιουργία βοηθητικών εργαλείων για την χρήση δεδομένων από αρχεία xml και την επεξεργασία τους με αλγορίθμους σμήνους για την εξαγωγή συμπερασμάτων. / The current post graduate thesis addresses the issue of school timetabling problem through the usage of PSO algorithms. In order to achieve greater efficiency complementary tool have been designed and developed.