Explicit Building Block Multiobjective Evolutionary Computation: Methods and Applications

This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Concentration is on concepts, design formulation, and prescription for multiobjective problem solving and explicit building block (BB) multiobjective evolutionary algorithms (MOEAs). Current state-of-the-art explicit BB MOEAs are addressed in the innovative design, execution, and testing of a new multiobjective explicit BB MOEA. Evolutionary computation concepts examined are algorithm convergence, population diversity and sizing, genotype and phenotype partitioning, archiving, BB concepts, parallel evolutionary algorithm (EA) models, robustness, visualization of evolutionary process, and performance in terms of effectiveness and efficiency. The main result of this research is the development of a more robust algorithm where MOEA concepts are implicitly employed. Testing shows that the new MOEA can be more effective and efficient than previous state-of-the-art explicit BB MOEAs for selected test suite multiobjective optimization problems (MOPs) and U.S. Air Force applications. Other contributions include the extension of explicit BB definitions to clarify the meanings for good single and multiobjective BBs. A new visualization technique is developed for viewing genotype, phenotype, and the evolutionary process in finding Pareto front vectors while tracking the size of the BBs. The visualization technique is the result of a BB tracing mechanism integrated into the new MOEA that enables one to determine the required BB sizes and assign an approximation epistasis level for solving a particular problem. The culmination of this research is explicit BB state-of-the-art MOEA technology based on the MOEA design, BB classifier type assessment, solution evolution visualization, and insight into MOEA test metric validation and usage as applied to test suite, deception, bioinformatics, unmanned vehicle flight pattern, and digital symbol set design MOPs

Short Term Load Forecasting Using Computational Intelligence Methods

Load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This dissertation focuses on study of short term load forecasting using different types of computational intelligence methods. It uses evolutionary algorithms (i.e. Genetic Algorithm, Particle Swarm Optimization, Artificial Immune System), neural networks (i.e. MLPNN, RBFNN, FLANN, ADALIN, MFLNN, WNN, Recurrent NN, Wilcoxon NN), and fuzzy systems (i.e. ANFIS). The developed methods give load forecasts of one hour upto 24 hours in advance. The algorithms and networks were have been demonstrated using simulation studies. The power sector in Orissa has undergone various structural and organizational changes in recent past. The main focus of all the changes initiated is to make the power system more efficient, economically viable and better service oriented. All these can happen if, among other vital factors, there is a good and accurate system in place for forecasting the load that would be in demand by electricity customers. Such forecasts will be highly useful in proper system planning & operations. The techniques proposed in this thesis have been simulated using data obtained from State Load Dispatch Centre, Orissa for the duration September – 2006 to August – 2007

Cost-sensitive weighting and imbalance-reversed bagging for streaming imbalanced and concept drifting in electricity pricing classification

National Natural Science Foundation of China Grants 61572201 and 51707041; Guangzhou Science and Technology Plan Project 201804010245; Fundamental Research Funds for the Central Universities 2017ZD052; Guangdong University of Technology Grant from the Financial and Education Department of Guangdong Province 2016[202]; Education Department of Guangdong Province project number 2016KCXTD022; State Grid Technology Project Grant 5211011600RJ

IoT-based platform for automated IEQ spatio-temporal analysis in buildings using machine learning techniques

Financiaciado para publicación en acceso aberto: Universidade de Vigo/CISUGProviding accurate information about the indoor environmental quality (IEQ) conditions inside building spaces is essential to assess the comfort levels of their occupants. These values may vary inside the same space, especially for large zones, requiring many sensors to produce a fine-grained representation of the space conditions, which increases hardware installation and maintenance costs. However, sound interpolation techniques may produce accurate values with fewer input points, reducing the number of sensors needed. This work presents a platform to automate this accurate IEQ representation based on a few sensor devices placed across a large building space. A case study is presented in a research centre in Spain using 8 wall-mounted devices and an additional moving device to train a machine learning model. The system yields accurate results for estimations at positions and times never seen before by the trained model, with relative errors between 4% and 10% for the analysed variables.Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C2Ministerio de Ciencia, Innovación y Universidades | Ref. FPU17/ 01834Ministerio de Ciencia, Innovación y Universidades | Ref. FPU19/01187Universidad de Vigo | Ref. 00VI 131H 641.0

Novel system of pavement cracking detection algorithms using 1mm 3D surface data

Pavement cracking is one of the major concerns for pavement design and management. There have been rapid developments of automated pavement cracking detection in recent years. However, none of them has been widely accepted so far due to lack of capability of maintaining consistently high detection accuracy for various pavement surfaces. Using 1mm 3D data collected by WayLink Digital Highway Data Vehicle (DHDV), an entire system of algorithms, which consists of Fully Automated Cracking Detection Subsystem, Interactive Cracking Detection Subsystem and Noisy Pattern Detection Subsystem, is proposed in this study for improvements in adaptability, reliability and interactivity of pavement cracking detection.The Fully Automated Cracking Detection Subsystem utilizes 3D Shadow Simulation to find lower areas in local neighborhood, and then eliminates noises by subsequent noise suppressing procedures. The assumption behind 3D Shadow Simulation is that local lower areas will be shadowed under light with a certain projection angle. According to the Precision-Recall Analysis on two real pavement segments, the fully automated subsystem can achieve a high level of Precision and Recall on both pavement segments.The Interactive Cracking Detection Subsystem implements an interactive algorithm proposed in this study, which is capable of improving its detection accuracy by adjustments based on the operator's feedback, to provide a slower but more flexible as well as confident approach to pavement cracking detection. It is demonstrated in the case study that the interactive subsystem can retrieve almost 100 percent of cracks with nearly no noises.The Noisy Pattern Detection Subsystem is proposed to exclude pavement joints and grooves from cracking detection so that false-positive errors on rigid pavements can be reduced significantly. This subsystem applies Support Vector Machines (SVM) to train the classifiers for the recognition of transverse groove, transverse joint, longitudinal groove and longitudinal joint respectively. Based on the trained classifiers, pattern extraction procedures are developed to find the exact locations of pavement joints and grooves.Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is one of multi objective genetic algorithms, is employed in this study to optimize parameters of the fully automated subsystem for the pursuing of high Precision and high Recall simultaneously. In addition to NSGA-II, an Auxiliary Prediction Model (APM) is proposed in this study to assist NSGA-II for faster convergence and better diversity.Finally, CPU-based and GPU-based Parallel Computing Techniques, including MultiGPU, GPU streaming, Multi-Core and Multi-Threading are combined in this study to increase the processing speed for all computational tasks that can be synchronous