Most optimisation problems in everyday life are not static in nature, have multiple objectives and at least two of the objectives are in conflict with one another. However, most research focusses on either static multi-objective optimisation (MOO) or dynamic singleobjective optimisation (DSOO). Furthermore, most research on dynamic multi-objective optimisation (DMOO) focusses on evolutionary algorithms (EAs) and only a few particle swarm optimisation (PSO) algorithms exist. This thesis proposes a multi-swarm PSO algorithm, dynamic Vector Evaluated Particle Swarm Optimisation (DVEPSO), to solve dynamic multi-objective optimisation problems (DMOOPs). In order to determine whether an algorithm solves DMOO efficiently, functions are required that resembles real world DMOOPs, called benchmark functions, as well as functions that quantify the performance of the algorithm, called performance measures. However, one major problem in the field of DMOO is a lack of standard benchmark functions and performance measures. To address this problem, an overview is provided from the current literature and shortcomings of current DMOO benchmark functions and performance measures are discussed. In addition, new DMOOPs are introduced to address the identified shortcomings of current benchmark functions. Guides guide the optimisation process of DVEPSO. Therefore, various guide update approaches are investigated. Furthermore, a sensitivity analysis of DVEPSO is conducted to determine the influence of various parameters on the performance of DVEPSO. The investigated parameters include approaches to manage boundary constraint violations, approaches to share knowledge between the sub-swarms and responses to changes in the environment that are applied to either the particles of the sub-swarms or the non-dominated solutions stored in the archive. From these experiments the best DVEPSO configuration is determined and compared against four state-of-the-art DMOO algorithms. / Thesis (PhD)--University of Pretoria, 2012. / Computer Science / unrestricted

Groups of starlings can form impressive shapes as they travel northward together in the springtime. This is among a group of natural phenomena based on swarm behaviour. The research field of artificial intelligence in computer science, particularly the areas of robotics and image processing, has in recent decades given increasing attention to the underlying structures. The behaviour of these intelligent swarms has opened new approaches for face detection as well. G. Beni and J. Wang coined the term “swarm intelligence” to describe this type of group behaviour. In this context, intelligence describes the ability to solve complex problems. The objective of this project is to automatically find exactly one face on a photo or video material by means of swarm intelligence. The process developed for this purpose consists of a combination of various known structures, which are then adapted to the task of face detection. To illustrate the result, a 3D hat shape is placed on top of the face using an example application program.:1 Introduction 1.1 Face Detection 1.2 Swarm Intelligence and Particle Swarm Optimisation Fundamentals 3 Face Detection by Means of Particle Swarm Optimisation 3.1 Swarms and Particles 3.2 Behaviour Patterns 3.2.1 Opportunism 3.2.2 Avoidance 3.2.3 Other Behaviour Patterns 3.3 Stop Criterion 3.4 Calculation of the Solution 3.5 Example Application 4 Summary and Outlook

Die Optimierung spielt in der Industrie und Technik eine entscheidende Rolle. Für einen Betrieb ist es beispielsweise äußerst wichtig, die zur Verfügung stehenden Ressourcen optimal zu nutzen und Betriebsabläufe effizient zu gestalten. Damit diese Vorhaben umgesetzt werden können, setzt man Methoden der Optimierung ein. Die Zielstellungen werden als eine abstrakte mathematische Aufgabe formuliert und anschließend wird versucht, dieses Problem mit einem Optimierungsverfahren zu lösen. Da die Komplexität der Problemstellungen in der Praxis ansteigt, sind exakte Verfahren in der Regel nicht mehr effizient anwendbar, sodass andere Methoden zum Lösen dieser Aufgaben entwickelt werden müssen, die in angemessener Zeit eine akzeptable Lösung finden. Solche Methoden werden als Approximationsalgorithmen bezeichnet. Im Gegensatz zu den exakten Verfahren ist der Verlauf der Optimierung bei dieser Verfahrensklasse vom Zufall abhängig. Dadurch lassen sich in der Regel keine Konvergenzaussagen beweisen. Dennoch hat sich gezeigt, dass Approximationsalgorithmen viel versprechende Ergebnisse für eine Vielzahl von unterschiedlichen Problemstellungen liefern. Zwei Approximationsalgorithmen werden in dieser Arbeit vorgestellt, untersucht und erweitert. Zum einen steht ein Verfahren im Vordergrund, welches aus Beobachtungen in der Natur entstanden ist. Es gibt Lebewesen, die durch verblüffend einfache Strategien in der Lage sind, komplexe Probleme zu lösen. Beispielsweise bilden Fische Schwärme, um sich vor Fressfeinden zu schützen. Der Fischschwarm kann dabei als selbstorganisierendes System verstanden werden, bei dem die Aktivitäten der einzelnen Fische hauptsächlich von den Bewegungen der Nachbarfische abhängig sind. An diesem erfolgreichen Schwarmverhalten ist der moderne Approximationsalgorithmus der Partikelschwarmoptimierung angelehnt. Weiterhin wird ein ersatzmodellgestütztes Verfahren präsentiert. Der Ausgangspunkt dieses Optimierungsverfahrens ist der Aufbau von Ersatzmodellen, um das Verhalten der Zielfunktionen anhand der bisherigen Auswertungen vorhersagen zu können. Damit so wenig wie möglich Funktionsauswertungen vorgenommen werden müssen, wird bei diesem Verfahren ein hoher Aufwand in die Wahl der Punkte investiert, welche auszuwerten sind. Die vorliegende Diplomarbeit gliedert sich wie folgt. Zunächst werden die mathematischen Grundlagen für das Verständnis der weiteren Ausführungen gelegt. Insbesondere werden multikriterielle Optimierungsaufgaben betrachtet und klassische Lösungsansätze aufgezeigt. Das dritte Kapitel beschäftigt sich mit der Partikelschwarmoptimierung. Dieser „naturanaloge Approximationsalgorithmus“ wird ausführlich dargelegt und analysiert. Dabei stehen die Funktionsweise und der Umgang mit mehreren Zielen und Restriktionen im Vordergrund der Ausarbeitung. Ein ersatzmodellgestütztes Optimierungsverfahren wird im Anschluss darauf vorgestellt und erweitert. Neben der Verfahrensanalyse, ist die Behebung der vorhandenen Schwachstellen ein vorrangiges Ziel dieser Untersuchung. Die eingeführten und implementierten Verfahren werden im fünften Kapitel an geeigneten analytischen und technischen Problemen verifiziert und mit anderen Approximationsalgorithmen verglichen. Anschließend werden Empfehlungen für die Verwendung der Verfahren gegeben. Die gewonnenen Kenntnisse werden im letzten Kapitel zusammengefasst und es wird ein Ausblick für zukünftige Forschungsthemen gegeben

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In recent years, the power industry has experienced significant changes on the distribution power system primarily due to the implementation of smart-grid technology and the incremental implementation of distributed generation. Distributed Generation (DG) is simply defined as the decentralization of power plants by placing smaller generating units closer to the point of consumption, traditionally ten mega-watts or smaller. While DG is not a new concept, DG is gaining widespread interest primarily for the following reasons: increase in customer demand, advancements in technology, economics, deregulation, environmental and national security concerns. The distribution power system traditionally has been designed for radial power flow, but with the introduction of DG, the power flow becomes bidirectional. As a result, conventional power analysis tools and techniques are not able to properly assess the impact of DG on the electrical system. The presence of DG on the distribution system creates an array of potential problems related to safety, stability, reliability and security of the electrical system. Distributed generation on a power system affects the voltages, power flow, short circuit currents, losses and other power system analysis results. Whether the impact of the DG is positive or negative on the system will depend primarily on the location and size of the DG. The objective of this research is to develop indices and an effective technique to evaluate the impact of distributed generation on a distribution power system and to employ the particle swarm optimization technique to determine the optimal placement and size of the DG unit with an emphasis on improving system reliability while minimizing the following system parameters: power losses, voltage deviation and fault current contributions. This research utilizes the following programs to help solve the optimal DG placement problem: Distribution System Simulator (DSS) and MATLAB. The developed indices and PSO technique successfully solved the optimal DG sizing and placement problem for the I 13-Node, 34-Node and 123-Node Test Cases. The multi-objective index proved to be computational efficient and accurately evaluated the impact of distributed generation on the power system. The results provided valuable information about the system response to single and multiple DG units.

<p>Efficient topic modeling is needed to support applications that aim at identifying main themes from a collection of documents. In this thesis, a reduced vector embedding representation and particle swarm optimization (PSO) are combined to develop a topic modeling strategy that is able to identify representative themes from a large collection of documents. Documents are encoded using a reduced, contextual vector embedding from a general-purpose pre-trained language model (sBERT). A modified PSO algorithm (pPSO) that tracks particle fitness on a dimension-by-dimension basis is then applied to these embeddings to create clusters of related documents. The proposed methodology is demonstrated on three datasets across different domains. The first dataset consists of posts from the online health forum r/Cancer. The second dataset is a collection of NY Times abstracts and is used to compare</p> <p>the proposed model to LDA. The third is a standard benchmark dataset for topic modeling which consists of a collection of messages posted to 20 different news groups. It is used to compare state-of-the-art generative document models (i.e., ETM and NVDM) to pPSO. The results show that pPSO is able to produce interpretable clusters. Moreover, pPSO is able to capture both common topics as well as emergent topics. The topic coherence of pPSO is comparable to that of ETM and its topic diversity is comparable to NVDM. The assignment parity of pPSO on a document completion task exceeded 90% for the 20News-Groups dataset. This rate drops to approximately 30% when pPSO is applied to the same Skip-Gram embedding derived from a limited, corpus specific vocabulary which is used by ETM and NVDM.</p>

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[pt] A decomposição de sulfatos vem ganhando notoriedade pela sua capacidade de geração limpa de H2 através dos ciclos termoquímicos. O entendimento do mecanismo de decomposição é relevante para futuros planejamentos em aplicações industriais. Além disso, a modelagem desses processos permite obter informações acerca da energia requerida para que os mesmos ocorram. Dentre os diferentes sistemas de reações de decomposição, observa-se que alguns deles são mais complexos do que outros, envolvendo a presença de fases intermediárias e múltiplas reações consecutivas ou simultâneas. Portanto, o presente trabalho se propõe a desenvolver uma metodologia para a modelagem da decomposição térmica de sistemas reacionais com diferentes níveis de complexidade: sulfato de alumínio, alúmen de potássio, mistura de sulfatos de alumínio e potássio, sulfato de zinco e sulfato de ferro (II). Os experimentos foram realizados utilizando análise termogravimétrica (TG) para ter o entendimento dos diferentes estágios de decomposição, utilizando os dados obtidos na etapa de modelagem. O modelo envolveu o uso de um conjunto de equações diferenciais para representar cada uma das reações que ocorrem na decomposição. A estimação dos parâmetros cinéticos feita pelo método de otimização por enxame de partículas. Os resultados indicaram que sistemas envolvendo a decomposição do sulfato de alumínio são catalisados na presença de sulfato de potássio. No caso do zinco, a dessulfatação do sulfato anidro ocorre em duas etapas, com a presença de um oxissulfato como uma fase intermediária. O sulfato de ferro (II) também apresenta uma decomposição complexa ao passar pela fase de sulfato de ferro (III) antes de ser completamento convertido em hematita. Todas as modelagens mostraram excelente ajuste aos dados experimentais, com R2 acima de 0.98 em todos os casos. / [en] The interest over of the decomposition of sulfates has increased due to its capacity of generating clean H2 through the thermochemical cycles. Understanding the decomposition mechanism is relevant to future industrial design and applications. Moreover, the modeling of these processes gives the information needed to know how much energy is required for the occurrence of the reactions. Among the different reaction systems, it is observed a range of complexity, with the presence of intermediate phases, and multiple consecutive or simultaneous reactions. Therefore, the present work proposed to develop a modeling methodology for the thermal decomposition of sulfates systems with different complexity levels: aluminum sulfate, potassium alum, mixture of aluminum sulfate and potassium sulfate, zinc sulfate, and iron (II) sulfate. The experiments were performed using thermogravimetric analysis (TGA) to understand the decomposition stages and use the data in the modeling step. The developed model consisted of a system of differential equations to describe every reaction taking place in the decomposition. The kinetic parameters estimation was made by using particle swarm optimization. The results indicate that potassium sulfate catalyzes the decomposition of aluminum sulfate. In the case of zinc, the desulfation of anhydrous zinc sulfate occurs in two stages, with the presence zinc oxysulfate as an intermediate phase. Iron (II) sulfate also shows a complex decomposition system, as it first decomposes into iron (III) sulfate before it is completely converted into hematite. All the modeling results displayed an excellent agreement with the experimental data, with R2 values above 0.98 for all cases.

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