Every year, certain animal and insect species flock together to make long-range migrations to reach their feeding or breeding grounds. A number of interesting observations can be made regarding this phenomenon. First, individuals tend to create large flocks, which can include millions of individuals in fishes, for these migrations. Second, migrations typically cover long distances. Third, despite all kinds of disturbances affecting the individuals during these migrations, the flocks can reach the very same breeding or feeding grounds with remarkable accuracy. Biological studies indicated that these animals mainly use the magnetic field of earth (among many other environmental cues) to determine the direction of their travel. It was also claimed that migrating in flocks has been the key factor behind the accuracy of reaching the same grounds at the end of the migration. In this thesis, we take a constructivist approach towards investigating the effects of flocking in long-range travels using a swarm of physical and simulated mobile robots. Specifically, we extend a self-organized flocking behavior that was developed by Turgut et al. (2008) that allows the long-range migration of a robotic swarm in space using the magnetic field of the earth. Using this behavior, we analyze how the accuracy of the robotic swarm reaching a particular &quot / breeding ground&quot / is affected by four factors / namely, (1) averaging through the heading alignment, (2) noise in sensing the homing direction, (3) differences in the characteristics of the individuals, and (4) disturbances caused by the proximal interactions of the robots during flocking. Through systematic experiments with physical and simulated robots, we analyze how these factors affect the accuracy along with the flock size and different sources of noise.

The fundamental character of excitation system is to provide the direct-current power for field windings for synchronous generators. Excitation control system controls the generator output voltage and reactive power by varying the field winding¡¦s currents. Therefore, it can improve the transient stability of power system. The thesis proposed a process for modeling and simulation on a brushless coal-fired unit, since the 40 years-old magnetic amplifier (Type WMA MAG-A-STAT) Automatic Voltage Regulator (AVR) was replaced by a Programmable Logical Controller based digital redundancy system, for the purpose to verify the excitation system model and dynamic response gains in the future power system study. To establish the generator excitation system and simulations on a popular software program MATLAB/SIMULINK, we wish to manipulate the effective and precise simulation test on a personal-computer and apply Particle Swarm Optimization (PSO) to find the global optimal solution for AVR controller settings. This thesis contributes in building a reliable excitation system model with dynamic response figures for power system network planning and dispatch.

In this dissertation, particle swarm optimization (PSO) is utilized for fractal image compression (FIC) and active contour model (ACM). The dissertation is divided into two parts. The first part is concerned with the FIC and the second part with ACM. FIC is promising both theoretically and practically for image compression. However, since the encoding speed of the traditional full search method is very time-consuming, FIC with full search is unsuitable for real-time applications. In this dissertation, several novel PSO-based approaches incorporating the edge property of the image blocks are proposed to speedup the encoder and preserve the image quality. Instead of the full search, a direction map is built according to the edge type of the image blocks, which directs the particles in the swarm to regions consisting of candidates of higher similarity. Therefore, the searching space is reduced and the speedup can be achieved. Also, since the strategy is performed according to the edge property, better visual effect can be preserved. Experimental results show that the visual-based particle swarm optimization speeds up the encoder 125 times faster with only 0.89 dB decay of image quality in comparison to the full search method. The second part of the dissertation is concerned with the active contour model for automatic object boundary identification. In the traditional methods for ACM, each control point searches its new position in a small nearby window. Consequently, the boundary concavities cannot be searched accurately. Some improvements have been made in the past to enlarge the searching space, yet they are still time-consuming. To overcome these drawbacks, a novel multi-population PSO technique is adopted in this dissertation to enhance the concavity searching capability and reduce the search time but in a larger searching window. In the proposed scheme, to each control point in the contour there is a corresponding swarm of particles with the best swarm particle as the new control point. The proposed optimizer not only inherits the spirit of the original PSO in each swarm but also shares information of the surrounding swarms. Experimental results demonstrate that the proposed method can improve the search of object concavities without extra computation time.

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.

D. Tech. Electrical Engineering. / Particle Swarm Optimisation (PSO) is based on a metaphor of social interaction such as birds flocking or fish schooling to search a space by adjusting the trajectories of individual vectors, called "particles" conceptualized as moving points in a multidimensional space. This thesis presents several algorithms/techniques to improve the PSO's global search ability. Simulation and analytical results confirm the efficiency of the proposed algorithms/techniques when compared to the other state of the art algorithms.

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Thesis submitted in compliance with the requirements for the Master's Degree in Technology: Electrical Engineering - Light Current, Durban University of Technology, Department of Electronic Engineering, 2008. / Linear control systems can be easily tuned using classical tuning techniques such as the Ziegler-Nichols and Cohen-Coon tuning formulae. Empirical studies have found that these conventional tuning methods result in an unsatisfactory control performance when they are used for processes experiencing the negative destabilizing effects of strong nonlinearities. It is for this reason that control practitioners often prefer to tune most nonlinear systems using trial and error tuning, or intuitive tuning. A need therefore exists for the development of a suitable tuning technique that is applicable for a wide range of control loops that do not respond satisfactorily to conventional tuning. Emerging technologies such as Swarm Intelligence (SI) have been utilized to solve many non-linear engineering problems. Particle Swarm Optimization (PSO), developed by Eberhart and Kennedy (1995), is a sub-field of SI and was inspired by swarming patterns occurring in nature such as flocking birds. It was observed that each individual exchanges previous experience, hence knowledge of the “best position” attained by an individual becomes globally known. In the study, the problem of identifying the PID controller parameters is considered as an optimization problem. An attempt has been made to determine the PID parameters employing the PSO technique. A wide range of typical process models commonly encountered in industry is used to assess the efficacy of the PSO methodology. Comparisons are made between the PSO technique and other conventional methods using simulations and real-time control.

Morphing an aircraft wingtip can provide substantial performance improvement. Most civil transport aircraft are optimized for range but for other flight conditions such as take-off and climb they are used as constraints. These constraints could potentially reduce the performance of an aircraft at cruise. By altering the shape of the wingtip, we can force the load distribution to adapt to the required flight condition to improve performance. Using a Variable Geometry Truss Mechanism (VGTM) concept to morph the wingtip of an aircraft with a Multidisciplinary Design Optimization (MDO) framework, the current work will attempt to find an optimal wing and wingtip shape to minimize fuel consumption for multiple morphing stages during cruise. This optimization routine was conducted with a Particle Swarm Optimization (PSO) algorithm using different fidelity tools to analyze the aerodynamic and structural disciplines.

Morphing an aircraft wingtip can provide substantial performance improvement. Most civil transport aircraft are optimized for range but for other flight conditions such as take-off and climb they are used as constraints. These constraints could potentially reduce the performance of an aircraft at cruise. By altering the shape of the wingtip, we can force the load distribution to adapt to the required flight condition to improve performance. Using a Variable Geometry Truss Mechanism (VGTM) concept to morph the wingtip of an aircraft with a Multidisciplinary Design Optimization (MDO) framework, the current work will attempt to find an optimal wing and wingtip shape to minimize fuel consumption for multiple morphing stages during cruise. This optimization routine was conducted with a Particle Swarm Optimization (PSO) algorithm using different fidelity tools to analyze the aerodynamic and structural disciplines.

This thesis describes the development and experimental results of a system to explore cloning of facial expressions between dissimilar face models, so new faces can be animated using the animations from existing faces. The system described in this thesis uses fuzzy membership functions and subtractive clustering to represent faces and expressions in an intermediate space. This intermediate space allows expressions for face models with different resolutions to be compared. The algorithm is trained for each pair of faces using particle swarm optimization, which selects appropriate weights and radii to construct the intermediate space. These techniques allow the described system to be more flexible than previous systems, since it does not require prior knowledge of the underlying implementation of the face models to function.