Water Resource Systems Analysis - University of Kentucky, Lexington

Graduate course in water resource systems analysis offered at University of Kentucky, Lexington in Fall 2015

Proceedings of the XIII Global Optimization Workshop: GOW'16

[Excerpt] Preface: Past Global Optimization Workshop shave been held in Sopron (1985 and 1990), Szeged (WGO, 1995), Florence (GO’99, 1999), Hanmer Springs (Let’s GO, 2001), Santorini (Frontiers in GO, 2003), San José (Go’05, 2005), Mykonos (AGO’07, 2007), Skukuza (SAGO’08, 2008), Toulouse (TOGO’10, 2010), Natal (NAGO’12, 2012) and Málaga (MAGO’14, 2014) with the aim of stimulating discussion between senior and junior researchers on the topic of Global Optimization. In 2016, the XIII Global Optimization Workshop (GOW’16) takes place in Braga and is organized by three researchers from the University of Minho. Two of them belong to the Systems Engineering and Operational Research Group from the Algoritmi Research Centre and the other to the Statistics, Applied Probability and Operational Research Group from the Centre of Mathematics. The event received more than 50 submissions from 15 countries from Europe, South America and North America. We want to express our gratitude to the invited speaker Panos Pardalos for accepting the invitation and sharing his expertise, helping us to meet the workshop objectives. GOW’16 would not have been possible without the valuable contribution from the authors and the International Scientific Committee members. We thank you all. This proceedings book intends to present an overview of the topics that will be addressed in the workshop with the goal of contributing to interesting and fruitful discussions between the authors and participants. After the event, high quality papers can be submitted to a special issue of the Journal of Global Optimization dedicated to the workshop. [...

(Global) Optimization: Historical notes and recent developments

Recent developments in (Global) Optimization are surveyed in this paper. We collected and commented quite a large number of recent references which, in our opinion, well represent the vivacity, deepness, and width of scope of current computational approaches and theoretical results about nonconvex optimization problems. Before the presentation of the recent developments, which are subdivided into two parts related to heuristic and exact approaches, respectively, we briefly sketch the origin of the discipline and observe what, from the initial attempts, survived, what was not considered at all as well as a few approaches which have been recently rediscovered, mostly in connection with machine learning

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Search-based Test Generation for Automated Driving Systems: From Perception to Control Logic

abstract: Automated driving systems are in an intensive research and development stage, and the companies developing these systems are targeting to deploy them on public roads in a very near future. Guaranteeing safe operation of these systems is crucial as they are planned to carry passengers and share the road with other vehicles and pedestrians. Yet, there is no agreed-upon approach on how and in what detail those systems should be tested. Different organizations have different testing approaches, and one common approach is to combine simulation-based testing with real-world driving. One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. Besides safety, there are other expectations from automated vehicles such as comfortable driving and minimal fuel consumption. All safety and functional expectations from an automated driving system should be captured with a set of system requirements. It is challenging to create requirements that are unambiguous and usable for the design, testing, and evaluation of automated driving systems. Another challenge is to define useful metrics for assessing the testing quality because in general, it is impossible to test every possible scenario. The goal of this dissertation is to formalize the theory for testing automated vehicles. Various methods for automatic test generation for automated-driving systems in simulation environments are presented and compared. The contributions presented in this dissertation include (i) new metrics that can be used to discover the boundary cases between safe and unsafe driving conditions, (ii) a new approach that combines combinatorial testing and optimization-guided test generation methods, (iii) approaches that utilize global optimization methods and random exploration to generate critical vehicle and pedestrian trajectories for testing purposes, (iv) a publicly-available simulation-based automated vehicle testing framework that enables application of the existing testing approaches in the literature, including the new approaches presented in this dissertation.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs

Monitoring and control of a photovoltaic panel in real time

The performance of the photovoltaic cell, among many factors, also depends on the accurate estimation of its internal and external parameters determined by I-V characterization, whose measurements are affected by noise and the resulting uncertainties. Their determination is of extreme importance and allows the monitoring of the maximum power point (MPP). The present work aims at designing an innovative numerical and simulation platform for op timal characterization purposes. It is a decision support software applied to the estimation of the photovoltaic cell five parameters. The modified Nelder-Mead Algorithm is implemented at its base, which allows the search for the optimum value of each parameter. Furthermore, through an experimental work, it is sought to show the performance of the developed algo rithm by applying indicators analysis, namely RMSE and R-squared. This algorithm will be also implemented in a low-cost embedded system where the purpose is to calculate the MPP; Resumo: Monitorização e controlo de um painel fotovoltaico em temo real - O desempenho da célula fotovoltaica, entre muitos fatores, depende também da estimativa precisa dos seus parâmetros internos e externos determinados pela caracterização I-V, cujas medições são afetadas pelo ruído e pelas incertezas resultantes. A sua determinação é de ex trema importância e permite a monitorização do ponto de potência máxima (MPP). O presente trabalho visa a conceção de uma inovadora plataforma numérica e de simulação para fins de caracterização ótima. Trata-se de um software de apoio à decisão aplicado à estimativa dos cinco parâmetros de uma célula fotovoltaica. Na sua base encontra-se imple mentado o Algoritmo Nelder-Mead modificado, o que permite a procura do valor ótimo de cada parâmetro. Além disso, através de um trabalho experimental, pretende-se mostrar o de sempenho do algoritmo desenvolvido aplicando a análise de indicadores, nomeadamente RMSE e R-squared. Este algoritmo será também implementado num sistema integrado de baixo custo com o objetivo de calcular o MPP

Individualizing assembly processes for geometric quality improvement

Dimensional deviations are a consequence of the mass production of parts. These deviations can be controlled by tightening production tolerances. However, this solution is not always desired because it usually increases production costs. The availability of massive amounts of data about products and automatized production has opened new opportunities to improve products\u27 geometrical quality by individualizing the assembly process. This individualization can be conducted through several techniques, including selective assembly, locator adjustments, weld sequence optimization, and clamping sequence optimization in a smart assembly line for spot-welded sheet metal assemblies. This study focuses on two techniques of individualizing the assembly process, selective assembly, and individualized locator adjustments in assembly fixtures. The existing studies and applications of these methods are reviewed, and the research gaps are defined. The previous applications of selective assembly are limited to linear and rigid assemblies. This study develops the application of selective assembly for sheet metal assemblies. This research addresses another research gap regarding the selective assembly of sheet metals by reducing the calculation cost associated with this technique. This study also develops a new locator adjustment method. This method utilizes scanned geometries of mating parts to predict the required adjustments. Afterward, a method for individualized adjustments is also developed. Considering applied and residual stresses during the assembly process as constraints is another contribution of this research to locator adjustments. These methods are applied to three industrial sample cases and the results evaluated. The results illustrate that individualization in locator adjustments can increase geometrical quality improvements three to four times.Accumulation of the potential improvements from both techniques in a smart assembly line is also evaluated in this study. The results indicate that combining the techniques may not increase the geometrical quality significantly relative to using only individualized locator adjustments.A crucial factor in the achievable improvements through individualization is the utilized assembly fixture layout. This study develops a method of designing the optimal fixture layout for sheet metal assemblies. Different design and production strategies are investigated to acquire the maximum potential for geometrical improvements through individualization in self-adjusting smart assembly lines

Evolutionary approaches for portfolio optimization

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. Markowitz’s mean variance (MV) model is widely regarded as the foundation of modern portfolio theory and provides a quantitative framework for portfolio optimization problems. In real market, investors commonly face real-world trading restrictions and it requires that the constructed portfolios have to meet trading constraints. When additional constraints are added to the basic MV model, the problem thus becomes more complex and the exact optimization approaches run into difficulties to deliver solutions within reasonable time for large problem size. By introducing the cardinality constraint alone already transformed the classic quadratic optimization model into a mixed-integer quadratic programming problem which is an NP-hard problem. Evolutionary algorithms, a class of metaheuristics, are one of the known alternatives for optimization problems that are too complex to be solved using deterministic techniques. This thesis focuses on single-period portfolio optimization problems with practical trading constraints and two different risk measures. Four hybrid evolutionary algorithms are presented to efficiently solve these problems with gradually more complex real world constraints. In the first part of the thesis, the mean variance portfolio model is investigated by taking into account real-world constraints. A hybrid evolutionary algorithm (PBILDE) for portfolio optimization with cardinality and quantity constraints is presented. The proposed PBILDE is able to achieve a strong synergetic effect through hybridization of PBIL and DE. A partially guided mutation and an elitist update strategy are proposed in order to promote the efficient convergence of PBILDE. Its effectiveness is evaluated and compared with other existing algorithms over a number of datasets. A multi-objective scatter search with archive (MOSSwA) algorithm for portfolio optimization with cardinality, quantity and pre-assignment constraints is then presented. New subset generations and solution combination methods are proposed to generate efficient and diverse portfolios. A learning-guided multi-objective evolutionary (MODEwAwL) algorithm for the portfolio optimization problems with cardinality, quantity, pre-assignment and round lot constraints is presented. A learning mechanism is introduced in order to extract important features from the set of elite solutions. Problem-specific selection heuristics are introduced in order to identify high-quality solutions with a reduced computational cost. An efficient and effective candidate generation scheme utilizing a learning mechanism, problem specific heuristics and effective direction-based search methods is proposed to guide the search towards the promising regions of the search space. In the second part of the thesis, an alternative risk measure, VaR, is considered. A non-parametric mean-VaR model with six practical trading constraints is investigated. A multi-objective evolutionary algorithm with guided learning (MODE-GL) is presented for the mean-VaR model. Two different variants of DE mutation schemes in the solution generation scheme are proposed in order to promote the exploration of the search towards the least crowded region of the solution space. Experimental results using historical daily financial market data from S &P 100 and S & P 500 indices are presented. When the cardinality constraints are considered, incorporating a learning mechanism significantly promotes the efficient convergence of the search