This diploma thesis deals with the modeling of planar antennas with special superstrates in the CST Microwave Studio. Attention is given to the electromagnetic bandgap (EBG) substrates. Firstly, the working principle of these special substrates and generally physical phenomena accompanying electromagnetic waves propagation in a periodic medium are analyzed. By modeling of basic EBG structures in CST their dispersion diagrams were obtained and afterwards compared to one another. The object of the second part of this thesis was modeling of a chosen antenna in CST. An Antenna placed first on the conventional and then on the EBG substrate - in both cases with special superstrates - is analyzed and compared to each other. In the third part of this thesis, the particle swarm optimization (PSO) technique was implemented in CST (VBA language), and was applied to the original design of the chosen antenna. In the final part of this thesis, the optimized antenna was modeled and analyzed in ANSOFT HFSS, and the results from both simulation programs are compared to each other.

The thesis deals with microwave resonators synthesis in Matlab environment. Due to the multi-objective optimization, the shape of the resonátor is subsequently changed to fit requested frequency properties. Particle swarm optimization and frequency domain finite element method are used. Accuracy of the different number of elements was tested. Values of several optimization parameters were balanced to reach the maximal efficiency of the optimization.

This thesis deals with radio network design for a chosen part of a map. Here map is represented by digital map file, which was created within the project DEM. First step is to calculate distances between points in chosen map. With help of optimization algorithms, appropriate position of transceiver in the map and parameters of radio systems are determined, also final coverage by radio signal, represented by intensity of electric field or received power in whole map. The optimization algorithm is used to find the best solution in terms of input parameters (e.g. power of transmitter, height of mast) and resulting coverage of land by radio signal.

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.

This master's thesis is about nature optimalization technigues. Evolution algortihms together with main thesis topic, Particle Swarm Optimization, is introduced in the following chapter. Car setup and simulator TORCS are introduced in next chapter. Design and implementation are introduced in next chapters. Destination of t master's thesis is finding optimal car setups for different curcuits.

Computational Intelligence has found a challenging testbed for various paradigms in the financial sector. Extensive research has resulted in numerous financial applications using neural networks and evolutionary computation, mainly genetic algorithms and genetic programming. More recent advances in the field of computational intelligence have not yet been applied as extensively or have not become available in the public domain, due to the confidentiality requirements of financial institutions. This study investigates how co-evolution together with the combination of par- ticle swarm optimisation and neural networks could be used to discover competitive security trading agents that could enable the timing of buying and selling securities to maximise net profit and minimise risk over time. The investigated model attempts to identify security trend reversals with the help of technical analysis methodologies. Technical market indicators provide the necessary market data to the agents and reflect information such as supply, demand, momentum, volatility, trend, sentiment and retracement. All this is derived from the security price alone, which is one of the strengths of technical analysis and the reason for its use in this study. The model proposed in this thesis evolves trading strategies within a single pop- ulation of competing agents, where each agent is represented by a neural network. The population is governed by a competitive co-evolutionary particle swarm optimi- sation algorithm, with the objective of optimising the weights of the neural networks. A standard feed forward neural network architecture is used, which functions as a market trend reversal confidence. Ultimately, the neural network becomes an amal- gamation of the technical market indicators used as inputs, and hence is capable of detecting trend reversals. Timely trading actions are derived from the confidence output, by buying and short selling securities when the price is expected to rise or fall respectively. No expert trading knowledge is presented to the model, only the technical market indicator data. The co-evolutionary particle swarm optimisation model facilitates the discovery of favourable technical market indicator interpretations, starting with zero knowledge. A competitive fitness function is defined that allows the evaluation of each solution relative to other solutions, based on predefined performance metric objectives. The relative fitness function in this study considers net profit and the Sharpe ratio as a risk measure. For the purposes of this study, the stock prices of eight large market capitalisation companies were chosen. Two benchmarks were used to evaluate the discovered trading agents, consisting of a Bollinger Bands/Relative Strength Index rule-based strategy and the popular buy-and-hold strategy. The agents that were discovered from the proposed hybrid computational intelligence model outperformed both benchmarks by producing higher returns for in-sample and out-sample data at a low risk. This indicates that the introduced model is effective in finding favourable strategies, based on observed historical security price data. Transaction costs were considered in the evaluation of the computational intelligent agents, making this a feasible model for a real-world application. Copyright / Dissertation (MSc)--University of Pretoria, 2010. / Computer Science / unrestricted

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

Particle Swam Optimization (PSO) is especially useful for rapid optimization of problems involving multiple objectives and constraints in dynamic environments. It regularly and substantially outperforms other algorithms in benchmark tests. This paper describes research leading to the application of PSO to the autonomous asset management problem in electronic warfare. The PSO speed provides fast optimization of frequency allocations for receivers and jammers in highly complex and dynamic environments. The key contribution is the simultaneous optimization of the frequency allocations, signal priority, signal strength, and the spatial locations of the assets. The fitness function takes into account the assets' locations in 2 dimensions, maximizing their spatial distribution while maintaining allocations based on signal priority and power. The fast speed of the optimization enables rapid responses to changing conditions in these complex signal environments, which can have real-time battlefield impact. Results optimizing receiver frequencies and locations in 2 dimensions have been successful. Current run-times are between 450ms (3 receivers, 30 transmitters) and 1100ms (7 receivers, 50 transmitters) on a single-threaded x86 based PC. Run-times can be substantially decreased by an order of magnitude when smaller swarm populations and smart swarm termination methods are used, however a trade off exists between run-time and repeatability of solutions. The results of the research on the PSO parameters and fitness function for this problem are demonstrated.