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Graphical Processing Units stand for the success of Artificial Neural Networks over the past decade and their broader application in the industry. Another promising field of Artificial Intelligence is Evolutionary Algorithms. Their parallelization ability is well known and has been successfully applied in practice. However, these attempts focused on multi-core and multi-machine parallelization rather than on the GPU. This work explores the possibilities of Evolutionary Algorithms parallelization on GPU. I propose implementation in PyTorch library, allowing to execute EA on both CPU and GPU. The proposed implementation provides the most common evolutionary operators for Genetic Algorithms, Real-Coded Evolutionary Algorithms, and Particle Swarm Op- timization Algorithms. Finally, I show the performance is an order of magnitude faster on GPU for medium and big-sized problems and populations. 1

Indiana University-Purdue University Indianapolis (IUPUI) / The power transmission network is stretched thin in Western North America. When generators or substations fault, the resultant cascading failures can diminish transmission capabilities across wide regions of the continent. This thesis examined several methods of determining one-shot controls based on frequency decline in electrical generators to reduce the effect of one or more phase faults and tripped generators. These methods included criteria based on indices calculated from frequency measured at the controller location. These indices included criteria based on local modes and the rate of change of frequency. This thesis primarily used particle swarm optimization (PSO) with inertia to determine a well-adapted set of parameters. The parameters included up to three thresholds for indices calculated from frequency. The researchers found that the best method for distinguishing between one or more phase faults used thresholds on two Fourier indices. Future lines of research regarding one-shot controls were considered. A method that distinguished nearby tripped generators from one or more phase faults and load change events was proposed. This method used a moving average, a negative threshold for control, and a positive threshold to reject control. The negative threshold for the moving average is met frequently during any large transient event. An additional index must be used to distinguish loss of generation events. This index is the maximum value of the moving average up to the present time and it is good for distinguishing loss of generation events from transient swings caused by other events. This thesis further demonstrated how well a combination of controls based on both rate of change of frequency and local modes reduces instability of the network as determined by both a reduction in RMSGA and control efficiency at any time after the events. This thesis found that using local modes is generally useful to diagnose and apply one-shot controls when instability is caused by one or more phase faults, while when disconnected generators or reduced loads cause instability in the system, the local modes did not distinguish between loss of generation capacity events and reduced load events. Instead, differentiating based on the rate of change of frequency and an initial upward deflection of frequency or an initial downward deflection of frequency did distinguish between these types of events.

The intention of the dissertation is the applied research of the collective ( group ) ( swarm ) intelligence . To demonstrate the applicability of the collective intelligence, the Particle Swarm Optimization ( PSO ) algorithm has been studied in which the problem of the collective intelligence is transferred to mathematical optimization in which the particle swarm searches for a global optimum within the defined problem space, and the searching is controlled according to the pre-defined objective function which represents the solved problem. A new search strategy has been designed and experimentally tested in which the particles continuously adjust their behaviour according to the characteristics of the problem space, and it has been experimentally discovered how the impact of the objective function representing a solved problem manifests itself in the behaviour of the particles. The results of the experiments with the proposed search strategy have been compared to the results of the experiments with the reference version of the PSO algorithm. Experiments have shown that the classical reference solution, where the only condition is a stable trajectory along which the particle moves in the problem space, and where the influence of a control objective function is ultimately eliminated, may fail, and that the dynamic stability of the trajectory of the particle itself is not an indicator of the searching ability nor the convergence of the algorithm to the true global solution of the solved problem. A search strategy solution has been proposed in which the PSO algorithm regulates its stability by continuous adjustment of the particles behaviour to the characteristics of the problem space. The proposed algorithm influenced the evolution of the searching of the problem space, so that the probability of the successful problem solution increased.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18568号 / 工博第3929号 / 新制||工||1604(附属図書館) / 31468 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 谷口 栄一, 准教授 宇野 伸宏, 准教授 山田 忠史 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

Achieving optimal performance of a database can be crucial for many businesses, and tuning its configuration parameters is a necessary step in this process. Many existing tuning methods involve complex machine learning algorithms and require large amounts of historical data from the system being tuned. However, training machine learning models can be problematic if a considerable amount of computational resources and data storage is required. This paper investigates the possibility of using less complex search algorithms or evolutionary algorithms to tune database configuration parameters, and presents a framework that employs Hill Climbing and Particle Swarm Optimization. The performance of the algorithms are tested on a PostgreSQL database using read-only workloads. Particle Swarm Optimization displayed the largest improvement in query response time, improving it by 26.09% compared to using the configuration parameters' default values. Given the improvement shown by Particle Swarm Optimization, evolutionary algorithms may be promising in the field of database tuning.

This dissertation develops new approaches for detection and classification of buried radioactive materials. Different spectral transformation methods are proposed to effectively suppress noise and to better distinguish signal features in the transformed space. The contributions of this dissertation are detailed as follows. 1) Propose an unsupervised method for buried radioactive material detection. In the experiments, the original Reed-Xiaoli (RX) algorithm performs similarly as the gross count (GC) method; however, the constrained energy minimization (CEM) method performs better if using feature vectors selected from the RX output. Thus, an unsupervised method is developed by combining the RX and CEM methods, which can efficiently suppress the background noise when applied to the dimensionality-reduced data from principle component analysis (PCA). 2) Propose an approach for buried target detection and classification, which applies spectral transformation followed by noisejusted PCA (NAPCA). To meet the requirement of practical survey mapping, we focus on the circumstance when sensor dwell time is very short. The results show that spectral transformation can alleviate the effects from spectral noisy variation and background clutters, while NAPCA, a better choice than PCA, can extract key features for the following detection and classification. 3) Propose a particle swarm optimization (PSO)-based system to automatically determine the optimal partition for spectral transformation. Two PSOs are incorporated in the system with the outer one being responsible for selecting the optimal number of bins and the inner one for optimal bin-widths. The experimental results demonstrate that using variable bin-widths is better than a fixed bin-width, and PSO can provide better results than the traditional Powell’s method. 4) Develop parallel implementation schemes for the PSO-based spectral partition algorithm. Both cluster and graphics processing units (GPU) implementation are designed. The computational burden of serial version has been greatly reduced. The experimental results also show that GPU algorithm has similar speedup as cluster-based algorithm.

Recent advances in electrical engineering have let the realization of small size electrical systems for in-body applications. Today’s hybrid implantable systems combine radio frequency and biosensor technologies. The biosensors are intended for wireless medical monitoring of the physiological parameters such as glucose, pressure, temperature etc. Enabling wireless communication with these biosensors is vital to allow continuous monitoring of the patients over a distance via radio frequency (RF) technology. Because the implantable antennas provide communication between the implanted device and the external environment, their efficient design is vital for overall system reliability. However, antenna design for implantable RF systems is a quite challenging problem due to antenna miniaturization, biocompatibility with the body’s physiology, high losses in the tissue, impedance matching, and low-power requirements. This dissertation presents design and measurement techniques of implantable antennas for medical wireless telemetry. A robust stochastic evolutionary optimization method, particle swarm optimization (PSO), is combined with an in-house finite-element boundary-integral (FE-BI) electromagnetic simulation code to design optimum implantable antennas using topology optimization. The antenna geometric parameters are optimized by PSO, and a fitness function is computed by FE-BI simulations to evaluate the performance of each candidate solution. For validating the robustness of the algorithm, in-vitro and in-vivo measurement techniques are also introduced. To illustrate this design methodology, two implantable antennas for wireless telemetry applications are considered. First, a small-size dual medical implant communications service (MICS) (402 MHz – 405 MHz) and industrial, scientific, and medical (ISM) (2.4 GHz – 2.48 GHz) band implantable antenna for human body is designed, followed by a dual band implantable antenna operating also in MICS and ISM bands for animal studies. In order to test the designed antennas in-vitro, materials mimicking the electrical properties of human and rat skins are developed. The optimized antennas are fabricated and measured in the materials. Moreover, the second antenna is in-vivo tested to observe the effects of the live tissue on the antenna performance. Simulation and measurement results regarding antenna parameters of the designed antennas such as return loss and radiation pattern are given and discussed in detail. The development details of the tissue-mimicking materials are also presented.

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