A predição de estruturas de proteínas (Protein Structure Prediction PSP) é um problema computacionalmente complexo. Para tratar esse problema, modelos simplificados de proteínas, como o Modelo HP, têm sido empregados para representar as conformações e Algoritmos Evolutivos (AEs) são utilizados na busca por soluções adequadas para PSP. Entretanto, abordagens utilizando AEs muitas vezes não tratam adequadamente as soluções geradas, prejudicando o desempenho da busca. Neste trabalho, é apresentada uma formulação multiobjetivo para PSP em Modelo HP, de modo a avaliar de forma mais robusta as conformações produzidas combinando uma avaliação baseada no número de contatos hidrofóbicos com a distância entre os monômeros. Foi adotado o Algoritmo Evolutivo Multiobjetivo em Tabelas (AEMT) a fim de otimizar essas métricas. O algoritmo pode adequadamente explorar o espaço de busca com pequeno número de indivíduos. Como consequência, o total de avaliações da função objetivo é significativamente reduzido, gerando um método para PSP utilizando Modelo HP mais rápido e robusto / Protein Structure Prediction (PSP) is a computationally complex problem. To overcome this drawback, simplified models of protein structures, such as the HP Model, together with Evolutionary Algorithms (EAs) have been investigated in order to find appropriate solutions for PSP. EAs with the HP Model have shown interesting results, however, they do not adequately evaluate potential solutions by using only the usual metric of hydrophobic contacts, hamming the performance of the algorithm. In this work, we present a multi-objective approach for PSP using HP Model that performs a better evaluation of the solutions by combining the evaluation based on the number of hydrophobic contacts with the distance among the hydrophobic amino acids. We employ a Multi-objective Evolutionary Algorithm based on Sub-population Tables (MEAT) to deal with these two metrics. MEAT can adequately explore the search space with relatively low number of individuals. As a consequence, the total assessments of the objective function is significantly reduced generating a method for PSP using HP Model that is faster and more robust

Em um ambiente de computação em nuvem, as empresas têm a capacidade de alocar recursos de acordo com a demanda. No entanto, há um atraso que pode levar alguns minutos entre o pedido de um novo recurso e o mesmo estar pronto para uso. Por esse motivo, as técnicas reativas, que solicitam um novo recurso apenas quando o sistema atinge um determinado limiar de carga, não são adequadas para o processo de alocação de recursos. Para resolver esse problema, é necessário prever as requisições que chegam ao sistema, no próximo período de tempo, para alocar os recursos necessários antes que o sistema fique sobrecarregado. Existem vários modelos de previsão de séries temporais para calcular as previsões de carga de trabalho com base no histórico de dados de monitoramento. No entanto, é difícil saber qual é o melhor modelo de previsão a ser utilizado em cada caso. A tarefa se torna ainda mais complicada quando o usuário não tem muitos dados históricos a serem analisados. A maioria dos trabalhos relacionados, considera apenas modelos de previsão isolados para avaliar os resultados. Outros trabalhos propõem uma abordagem que seleciona modelos de previsão adequados para um determinado contexto. Mas, neste caso, é necessário ter uma quantidade significativa de dados para treinar o classificador. Além disso, a melhor solução pode não ser um modelo específico, mas sim uma combinação de modelos. Neste trabalho propomos um método de previsão adaptativo, usando técnicas de otimização multiobjetivo, para combinar modelos de previsão de séries temporais. O nosso método não requer uma fase prévia de treinamento, uma vez que se adapta constantemente a medida em que os dados chegam ao sistema. Para avaliar a nossa proposta usamos quatro logs extraídos de servidores reais. Os resultados mostram que a nossa proposta frequentemente converge para o melhor resultado, e é suficientemente genérica para se adaptar a diferentes tipos de séries temporais. / In a cloud computing environment, companies have the ability to allocate resources according to demand. However, there is a delay that may take minutes between the request for a new resource and it is ready for using. The reactive techniques, which request a new resource only when the system reaches a certain load threshold, are not suitable for the resource allocation process. To address this problem, it is necessary to predict requests that arrive at the system in the next period of time to allocate the necessary resources, before the system becomes overloaded. There are several time-series forecasting models to calculate the workload predictions based on history of monitoring data. However, it is difficult to know which is the best time series forecasting model to be used in each case. The work becomes even more complicated when the user does not have much historical data to be analyzed. Most related work considers only single methods to evaluate the results of the forecast. Other work propose an approach that selects suitable forecasting methods for a given context. But in this case, it is necessary to have a significant amount of data to train the classifier. Moreover, the best solution may not be a specific model, but rather a combination of models. In this work we propose an adaptive prediction method using multi-objective optimization techniques to combine time-series forecasting models. Our method does not require a previous phase of training, because it constantly adapts the extent to which the data is coming. To evaluate our proposal we use four logs extracted from real servers. The results show that our proposal often brings the best result, and is generic enough to adapt to various types of time series.

The global energy demand in residential sector is increasing steadily every year due to advancement in technologies. The present electricity grid is designed to support peak demand rather than Peak to Average (PAR) demand. Utilities are investigating the residential Demand Response (DR) to lower the (PAR) ratio and eliminate the need of building new power infrastructure. This requires Home Energy Management System (HEMS) at grid edge to manage and control the energy demand. In this thesis, we presented an MDPSO based DR enabled HEMS model for optimal allocation of energy resources in a smart dwelling. The algorithm is designed to lower peak energy demand as well as encourage the active participation of customers by offering a reward to comply with DR request. We categorized appliances as elastic non-deferrable loads and inelastic deferrable loads based on their DR potential and operating characteristics. The scheduling of elastic and inelastic class of appliances is performed separately using canonical and binary version of PSO given how we expressed out load categories. We performed use case simulation to validate the performance of MDPSO for combination of different tariffs: Time of Use (TOU), TOU and Critical peak rebate signal (CPR), TOU and upper demand limit. Simulation results show that algorithm can reduce the electricity cost in range of 28% to 7% under increasing comfort conditions in response to TOU prices and Peak demand reduction of about 24% under TOU pricing and medium comfort conditions for single household. Under CPR DR requests, with respect to TOU pricing, there is effectively no change in the peak under the minimum comfort scenario. Furthermore, algorithm is able to suppress the peak upto 25% under combination of TOU and hard constraint on maximum power withdrawn from grid with no change in the electricity cost. Scheduling of multiple houses under TOU pricing results in peak reduction of 7 % as compared to baseline state. Under combination of TOU and CPR the aggregate peak energy demand of multiple households during DR activation time intervals is reduced by 32 %. The algorithm can suppress the peak demand by 27% under TOU and hard constraint on maximum power withdrawn from grid by multiple houses.

Over the past five decades, the Saudi government has adopted many agricultural policies aimed to: achieve self-sufficiency of food, increase the participation of the agricultural sector in the economy, and reduce the consumption of irrigation water. Due to conflicts among government objectives and the incompatibility of farmers' objectives with those of some agricultural policies, the government has not been able to fully achieve its objectives. To accomplish its goals the government, or decision maker needs to understand the farmer, or follower, reaction when s/he adopts a new decision. The dissertation aims to build a model that achieves government goals of minimizing the total irrigation water used while improving the total revenue from agricultural production, while incorporating farmers’ objective of maximizing their profit. To do this, linear programming and bi-level multi-objective multi-follower models are developed and applied to six regions of Saudi Arabia, which account for around 70 percent of cropland and consume about 13.131 BCM of irrigation water per year. The result of the linear programming model applied to the Riyadh region shows there is an unobserved factor effect on the farmers’ decisions, including irrigation water demand that comes from the presence of indirect subsidies. On the other hand, the bi-level multi-objective, multi-follower model shows there is the possibility to minimize irrigation water consumption while maintaining current total revenue from crop production through reallocating irrigation water among regions, while applying a variety of crop specific tax and subsidy policies among the regions to alter planting decisions.

Matching people to their preferences is an algorithmic topic with real world applications. One such application is the kidney exchange. The best "cure" for patients whose kidneys are failing is to replace it with a healthy one. Unfortunately, biological factors (e.g., blood type) constrain the number of possible replacements. Kidney exchanges seek to alleviate some of this pressure by allowing donors to give their kidney to a patient besides the one they most care about and in turn the donor for that patient gives her kidney to the patient that this first donor most cares about. Roth et al.~first discussed the classic kidney exchange problem. Freedman et al.~expanded upon this work by optimizing an additional objective in addition to maximal matching. In this work, I implement the traditional kidney exchange algorithm as well as expand upon more recent work by considering multi-objective optimization of the exchange. In addition I compare the use of 2-cycles to 3-cycles. I offer two hypotheses regarding the results of my implementation. I end with a summary and a discussion about potential future work.

Considering the significant investment of higher education made by students and their families, graduating in a timely manner is of the utmost importance. Delay attributed to drop out or the retaking of a course adds cost and negatively affects a student’s academic progression. Considering this, it becomes paramount for institutions to focus on student success in relation to term scheduling. Often overlooked, complexity of a course schedule may be one of the most important factors in whether or not a student successfully completes his or her degree. More often than not students entering an institution as a first time full time (FSFT) freshman follow the advised and published schedule given by administrators. Providing the optimal schedule that gives the student the highest probability of success is critical. In efforts to create this optimal schedule, this thesis introduces a novel optimization algorithm with the objective to separate courses which when taken together hurt students’ pass rates. Inversely, we combine synergistic relationships that improve a students probability for success when the courses are taken in the same semester. Using actual student data at the University of Kentucky, we categorically find these positive and negative combinations by analyzing recorded pass rates. Using Julia language on top of the Gurobi solver, we solve for the optimal degree plan of a student in the electrical engineering program using a linear and non-linear multi-objective optimization. A user interface is created for administrators to optimize their curricula at main.optimizeplans.com.

Traffic signal systems serve as one of the most powerful control tools in improving the efficiency of surface transportation travel. Traffic operations on arterial roads are particularly complex because of traffic interruptions caused by signalized intersections along the corridor. This dissertation research presents a systematic framework of integrated traffic control in an attempt to break down the complexities into several simpler sub-problems such as pattern recognition, environment-mobility relationships and multi-objective optimization for multi-criterial signal timing design. The overall goal of this dissertation is to develop signal timing plans, including a day plan schedule, cycle length parameters, splits and offsets, which are suitable for real traffic conditions with consideration of multi-criterial performance of the surface transportation system. To this end, the specific objectives are to: (1) identify appropriate time-of-day breakpoints and intervals to accommodate traffic pattern variations for day plan schedule of signal timing; (2) explore the relationship between environmental outcomes (e.g., emissions) from emission estimators and mobility measures (e.g., delay and stops) for different types of intersections; (3) optimize signal timing parameters for multi-criteria objectives (e.g., minimizing vehicular delay, number of stops, marginal costs of emissions and total costs), with the comparison of performance metrics for different objectives, at the intersection level; (4) optimize arterial offsets for different objectives at the arterial level and compare the performance metrics of different objectives to recommend suitable objectives for integrated multi-criteria signal timing design in arterial traffic operations. An extensive review of the literature, which covers existing tools, traffic patterns, traffic control with environmental concerns, and related optimization methods, shows that both opportunities and challenges have emerged for multi-criteria traffic signal timing design. These opportunities include large quantities of traffic condition data collected by system detectors or non-intrusive data collection platforms as well as powerful tools for microscopic traffic modeling and instantaneous emission estimation. The challenge is how to effectively deal with these big data, either from field collection or detailed simulation, and provide useful information for decision makers in practice. Methodologically, there's a tradeoff between the accuracy of objective function values and the computational efficiency of simulation and optimization. To address this need, in this dissertation, traffic signal timing design that systematically enables the use of integrated data and models are investigated and analyzed in the four steps/studies. The technology of identifying time-of-day breakpoints in the first study shows a mathematical way to classify dynamic traffic patterns by understanding dynamic traffic features and instabilities at a macroscopic level on arterials. Given the limitations of using built-in emissions modules within current traffic simulation and signal optimization tools, the metamodeling-based approach presented in the second study makes a methodological contribution. The findings of the second study on environment-mobility relationships set up the base for extensive application of two-stage optimization in the third and fourth studies for sustainable traffic operations and management. The comparison of outputs from an advanced estimator with those from the current tool also addresses improving the emissions module for more accurate analysis (e.g., benefit-cost analysis) in practical signal retiming projects. The third study shows that there are tradeoffs between minimizing delay and minimizing marginal costs of emissions. When total cost (including cost of delay, fuel consumption and emissions) is set as a single objective function, that objective clears the way for relatively reliable results for all the aspects. In the fourth study, the improvements in marginal cost of emissions and total cost by dynamic programming procedure are obvious, which indicates the effectiveness of using total link cost as an objective at the corridor level. In summary, this dissertation advocates a sustainable traffic control system by simultaneously considering travel time, fuel consumption and emissions. The outcomes of this integrated multi-criteria signal timing design can be easily implemented by traffic operators in their daily life of retiming signal timing.

Evolutionära algoritmer har visat sig vara effektiva för att utveckla spelnivåer. Dock finns fortfarande ett behov av nivåer som både uppfyller de krav som spelen har, samt att nivåerna som skapas ska vara så olika som möjligt för att uppmuntra upprepade spelomgångar. För att åstadkomma detta kan man använda Novelty Search. Dock saknar Novelty Search funktioner som gör att populationen vill uppfylla de krav som nivåerna ska ha. Arbetet fokuserar därför på att jämföra två Novelty Search baserade algoritmer som båda uppmuntrar kravuppfyllning: Feasible Infeasible Novelty Search (FINS) och Pareto based Multi-objective evolutionary algorithm (PMOEA) med två mål: krav och Novelty Search. Studien jämför algoritmerna utifrån tre värden: hur stor andel av populationen som följer de ställda kraven, hur bra dessa individer är på att lösa ett nivårelaterat problem samt diversiteten bland dessa individer. Utöver PMOEA och FINS implementeras även en Novelty Search algoritm och en traditionell evolutionär algoritm. Tre experiment genomförs där nivåernas storlek och antalet krav varierade. Resultatet visar att PMOEA var bättre på att skapa fler individer som följde alla kraven och att dessa individer överlag var bättre på att optimera lösningar än vanlig Novelty Search och FINS. Dock hade FINS högre diversitet bland individerna än alla algoritmerna som testades. Studiens svaghet är att resultatet är subjektivt till algoritmernas uppsättning i artefakten, som sådan borde framtida arbeten fokusera på att utforska nya uppsättningar för att generalisera resultatet.

The shareholders of E&P companies evaluate the future performance of these companies in terms of multiple performance attributes. Hence, E&P decision makers have the task of allocating limited resources to available project proposals to deliver the best performance on these various attributes. Additionally, the performance of these proposals on these attributes is uncertain and the attributes of the various proposals are usually correlated. As a result of the above, the E&P portfolio optimisation decision setting is characterised by multiple attributes with uncertain future performance. Most recent contributions in the E&P portfolio optimisation arena seek to adapt modern financial portfolio theory concepts to the E&P project portfolio selection problem. These contributions generally focus on understanding the tradeoffs between risk and return for the attribute NPV while acknowledging the presence of correlation among the assets of the portfolio. The result is usually an efficient frontier where one objective is set over the expected value of the NPV and the other is set over a risk metric calculated from the same attribute where, typically, the risk metric has a closed form solution (e.g., variance, standard deviation, semi-standard deviation). However, this methodology fails to acknowledge the presence of multiple attributes in the E&P decision setting. To fill this gap, this thesis proposes a decision support model to optimise risk and return objectives extracted from the NPV attribute and from other financial and/or operational attributes simultaneously. The result of this approach is an approximate Pareto front that explicitly shows the tradeoffs among these objectives whilst honouring intra-project and inter-project correlations. Intra-project correlations are incorporated into the optimisation by integrating the single project models to the portfolio model to be optimised. Inter-project correlation is included by modelling of the oil price a global variable. Additionally, the model uses a multi-objective simulation-optimisation approach and hence it overcomes the need of using risk metrics with closed form solutions. The model is applied to a set of realistic hypothetical offshore E&P projects. The results show the presence of complex relationships among the objectives in the approximate Pareto set. The ability of the method to unveil these relationships hopes to bring more insight to the decision makers and hence promote better investment decisions in the E&P industry. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1320463 / Thesis (M.Eng.Sc.) -- University of Adelaide, Australian School of Petroleum, 2008

<p>Water is essential for all life on earth. Global population increase and climate change are projected to increase the water stress, which already today is very high in many areas of the world. The differences between the largest and smallest global runoff estimates exceed the highest continental runoff estimates. These differences, which are caused by different modelling and measurement techniques together with large natural variabilities need to be further addressed. This thesis focuses on global water balance models that calculate global runoff, evaporation and water storage from precipitation and other climate data.</p><p>A new global water balance model, WASMOD-M was developed. Already when tuned against the volume error it reasonable produced within-year runoff patterns, but the volume error was not enough to confine the model parameter space. The parameter space and the simulated hydrograph could be better confined with, e.g., the Nash criterion. Calibration against snow-cover data confined the snow parameters better, although some equifinality still persisted. Thus, even the simple WASMOD-M showed signs of being overparameterised. </p><p>A simple regionalisation procedure that only utilised proximity contributed to calculate a global runoff estimate in line with earlier estimations. The need for better specifications of global runoff estimates was highlighted. </p><p>Global modellers depend on global data-sets that can have low quality in many areas. Major sources of uncertainty are precipitation and river regulation. A new routing method that utilises high-resolution flow network information in low-resolution calculations was developed and shown to perform well over all spatial scales, while the standard linear reservoir routing decreased in performance with decreasing resolution. This algorithm, called aggregated time-delay-histogram routing, is intended for inclusion in WASMOD-M.</p>