A distributed variant of multi-objective particle swarm optimization (MOPSO) called multi-objective parallel asynchronous particle swarm optimization (MOPAPSO) is presented, and the effects of distribution of objective function calculations to slave processors on the results and performance are investigated and employed for the synthesis of Grashof mechanisms. By using a formal multi-objective handling scheme based on Pareto dominance criteria, the need to pre-weight competing systemic objective functions is removed and the optimal solution for a design problem can be selected from a front of candidates after the parameter optimization has been completed. MOPAPSO's ability to match MOPSO's results using parallelization for improved performance is presented. Results for both four and ve bar mechanism synthesis examples are shown. / UOIT

An Equivalent Current Injection¡]ECI¡^based Preventive Security- Constrained Optimal Power Flow¡]PSCOPF¡^is presented in this paper and a particle swarm optimization (PSO) algorithm is developed for solving non-convex Optimal Power Flow (OPF) problems. This thesis integrated Simulated Annealing Particle Swarm Optimization¡]SAPSO¡^ and Multiple Particle Swarm Optimization¡]MPSO¡^, enabling a fast algorithm to find the global optimum. Optimal power flow is solved based on Equivalent- Current Injection¡]ECIOPF¡^algorithm. This OPF deals with both continuous and discrete control variables and is a mixed-integer optimal power flow¡]MIOPF¡^. The continuous control variables modeled are the active power output and generator-bus voltage magnitudes, while the discrete ones are the shunt capacitor devices. The feasibility of the proposed method is exhibited for a standard IEEE 30 bus system, and it is compared with other stochastic methods for the solution quality. Security Analysis is also conducted. Ranking method is used to highlight the most severe event caused by a specific fault. A preventive algorithm will make use of the contingency information, and keep the system secure to avoid violations when fault occurs. Generators will be used to adjust the line flow to the point that the trip of the most severe line would not cause a major problem.

Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market.

No description available.

In direct-sequence code division multiple access (DS-CDMA) systems, the heuristic optimization algorithms for multiuser detection include genetic algorithms (GA) and simulated annealing (SA) algorithm. In this thesis, we use particle swarm optimization (PSO) algorithms to solve the optimization problem of multiuser detection (MUD). PSO algorithm has several advantages, such as fast convergence, low computational complexity, and good performance in searching optimum solution. In order to enhance the performance and reduce the number of parameters, we propose two modified PSO algorithms, inertia weighting controlled PSO (W-PSO) and reduced-parameter PSO (R-PSO). From simulation results, the performance of our proposed algorithms can achieve that of optimal solution. Furthermore, our proposed algorithms have faster convergence performance and lower complexity when compared with other conventional algorithms.

The construction of Gene Regulatory Networks (GRNs) is one of the most important issues in systems biology. To infer a large-scale GRN with a nonlinear mathematical model, researchers need to encounter the time-consuming problem due to the large number of network parameters involved. In recent years, the cloud computing technique has been widely used to solve large-scale problems. Among others, Hadoop is currently the most well-known and reliable cloud computing framework, which allows users to analyze large amount of data in a distributed environment (i.e., MapReduce). It also supports data backup and data recovery mechanisms. This study proposes an Island-based GAPSO algorithm under the Hadoop cloud computing environment to infer large-scale GRNs. GAPSO exploited the position and velocity functions of PSO, and integrated the operations of Genetic Algorithm. This approach is often used to derive the optimal solution in nonlinear mathematical models. Several sets of experiments have been conducted, in which the number of network nodes varied from 50 to 125. The experiments were executed in the Hadoop distributed environment with 10, 20, and 26 computers, respectively. In the experiments of inferring the network with 125 gene nodes on the largest Hadoop cluster (i.e. 26 computers), the proposed framework performed up to 9.7 times faster than the stand-alone computer. It means that our work can successfully reduce 90% of the computation time in a single experimental run.

Due to the recent interest in broadband antennas a microstrip patch antenna was developed to meet the need for a cheap, low profile, broadband antenna. This antenna could be used in a wide range of applications such as in the communications industry for cell phones or satellite communication. Particle Swarm Optimization was used to design the dual-linear polarization broadband microstrip antenna and impedance matching network. This optimization method greatly reduced the time needed to find viable antenna parameters. A dual input patch antenna with over 30% bandwidth in the X-band was simulated using Ansoft's High Frequency Structural Simulator (HFSS) in conjunction with Particle Swarm Optimization. A single input and a dual input antenna was then fabricated. The fabricated antennas were composed of stacked microstrip patches over a set of bowtie apertures in the ground plane that were perpendicular to one another. A dual offset microstrip feedline was used to feed the aperture. Two different layers were used for the microstrip feedline of each polarization. The resulting measured impedance bandwidth was even wider than predicted. The antenna pattern was measured at several frequencies over the antenna bandwidth and was found to have good gain, consistent antenna patterns and low cross polarization.

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.

No description available.