The aim of this thesis is establishment of an overall framework of a novel control parameter optimization of automotive engine. Today, control parameters of an automotive engine have to be adjusted adequately and simultaneously to achieve plural criteria such as environmental emissions, fuel-consumption and engine torque. This process is called 'engine calibration'. Because many electronic control devices have been adopted for engine to satisfy these objectives, the complexity of engine calibration is increasing year to year. Recent progress in automatic control and instrumentation provides a smart environment called Hardware In the Loop Simulation (HILS) for engine calibration. In addition, Response Surface Methodology (RSM) based on statistical model is currently employed as the optimization method. Nevertheless, this approach is complicated by adequate model selection, precise model construction, and close model validation to confirm the precision of the model output. To cope with these problems, we noticed experiment-based optimization via HILS environment based on Multi-objective Evolutionary Algorithms (MOEAs), that is expected to be a powerful optimization framework for real world problems such as engineering design, as another automatic calibration approach. In experiment-based optimization, the parameters of a real system are optimized directly by optimization techniques in real time through experimentation. In this thesis, this approach is called Experiment-Based Evolutionary Multi-objective Optimization (EBEMO) and it is proposed as a novel automatic engine calibration technique. This approach can release us from burdens of model selection, construction, and validation. When using this technique, calibration can be done immediately after specifications have been changed after optimization. Hence, EBEMO promises to be an effective approach to automatic engine calibration. However, since conventional MOEAs face several difficulties, it is not easy to apply it to real engines. On the one hand, deterioration factors of the search performance of MOEAs in real environments have to be considered. For example, the observation noise of sensors included in output interferes with convergence of MOEAs. In addition, transient response by parameter switching also has similar harmful effects. Moreover, the periodicity of control inputs increase the complexity of the problems. On the other hand, the search time of MOEAs in real environments has to reduce because MOEAs require a tremendous number of evaluations. While we can obtain many measurements with HILS, severe limitations in the number of fitness evaluations still exist because the real experiments need real-time evaluations. Therefore, it is difficult to obtain a set of Pareto optimal solutions in practical time with conventional MOEAs. Additionally, plural MOPs defined by plural operating conditions of map-based controllers has to be optimized. In this thesis, to overcome the difficulties and to make EBEMO using the HILS environment feasible, five techniques are proposed. Each technique is developed through problem formulation, and their effectiveness are confirmed via numerical and real engine experiments. First, observation noise handling technique for MOEAs is considered. Because observation noise deteriorates the search ability of MOEAs, a memorybased fitness estimation method to exclude observation noise is introduced. Then, a crossover operator for periodic functions is proposed. Periodicity exists in engineering problems and leads to harmful effects on the performance of evolutionary algorithms. Moreover, the influence of transient response caused by parameter switching for dynamical systems is considered. In order to solve this problem, a solver of traveling salesman problems is used to determine the evaluation order of individuals. In addition, Pre-selection as acceleration method of MOEAs is proposed. In this technique, the generated offspring are pre-evaluated in the approximation model made by the search history, and then the promising offspring are evaluated in a real environment. Finally, parameterization of multi-objective optimization problems is considered. In engine calibration for maps, optimal control parameters have to be obtained at each operating condition such as engine speed and torque. This problem can be formulated in a form that needs to solve all of the plural multi-objective optimization problems defined by plural conditional variables. To solve this problem effectively, an interpolative initialization method is proposed. Through the real engine experiments, it was confirmed that EBEMO can achieve a practical search accuracy and time by using proposed techniques. In conclusion, the contribution of EBEMO for engine calibration is discussed. Additionally, the directions for future work are outlined. / Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(情報学) / 甲第14187号 / 情博第320号 / 新制||情||61(附属図書館) / 26493 / UT51-2008-N504 / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 喜多 一, 教授 酒井 徹朗, 教授 片井 修 / 学位規則第4条第1項該当

Wireless sensor networks (WSNs) are an emerging area of technology that have applications across many domains. By adding a mobile platform to the WSN we can increase its capabilities. One such scenario involves a mobile platform relocating sensors to fill sensing holes that are the result of sensor failure. We examine this problem, known as robot-assisted sensor relocation (RASR), and propose our own, multi-objective version, that we call reliable robot-assisted sensor relocation. We solve this problem using a set of state-of-the-art evolutionary multi-objective optimization algorithms. Additionally, we examine the multi-robot model, which we christen reliable multiple robot-assisted sensor relocation (RMRASR). The works collected within define these problems as well as provide empirical insight into the performance of well-known algorithms using these problems as a test-bed.

In this article, a design methodology for complex composite aircraft structures is presented. The developed approach combines a multi-objective optimization method and a parameterized simulation model using a design concept database. Due to the combination of discrete and continuous design variables describing the structures, evolutionary algorithms are used within the presented optimization approach. The approach requires an evaluation of the design alternatives that is performed by parameterized simulation models. The variability of these models is achieved using a design concept database that contains different layouts for each implemented structural part. Due to the complexity of the generated aircraft structures, the finite element method is applied for the calculation of the structural behaviour. The applicability of the developed design approach will be demonstrated by optimizing two composite aircraft fuselage examples. The obtained results show that the developed methodology is useful and reliable for designing complex aircraft structures.

The land use policies and development plans that are implemented in a city contribute to whether the city will be sustainable in the future. Therefore, when these policies are being established they should consider the potential impact on development. An analytical tool, such as land use change models, allow decision-makers to see the possible impact that these policies could have on development. Land use change models like UrbanSim make use of the relationship between households, buildings, and employment opportunities to model the decisions that people make on where to live and work. To be able to do this the model needs accurate data. When there is a more accurate location for the employment opportunities in an area, the decisions made by individuals can be better modelled and therefore the projected results are expected to be better. Previous research indicated that the methods that are traditionally used to disaggregate employment data to a lower level in UrbanSim projects are not applicable in the South African context. This is because the traditional methods require a detailed employment dataset for the disaggregation and this detailed employment dataset is not available in South Africa. The aim of this project was to develop a methodology for a metropolitan municipality in South Africa that could be used to disaggregate the employment data that is available at a higher level to a more detailed building level. To achieve this, the methodology consisted of two parts. The first part of the methodology was establishing a method that could be used to prepare a base dataset that is used for disaggregating the employment data. The second part of the methodology was using a multi-objective optimisation approach to allocate the number of employment opportunities within a municipality to building level. The algorithm was developed using the Distributed Evolutionary Algorithm in Python (DEAP) computational framework. DEAP is an open-source evolutionary algorithm framework that is developed in Python and enables users to rapidly create prototypes by allowing them to customise the algorithm to suit their needs The evaluation showed that it is possible to make use of multi-objective optimisation to disaggregate employment data to building level. The results indicate that the employment allocation algorithm was successful in disaggregating employment data from municipal level to building level. All evolutionary algorithms come with some degree of uncertainty as one of the main features of evolutionary algorithms is that they find the most optimal solution, and so there are other solutions available as well. Thus, the results of the algorithm also come with that same level of uncertainty. By enhancing the data used by land use change models, the performance of the overall model is improved. With this improved performance of the model, an improved view of the impact that land use policies could have on development can also be seen. This will allow decision-makers to draw the best possible conclusions and allow them the best possible opportunity to develop policies that will contribute to creating sustainable and lasting urban areas. / Dissertation (MSc (Geoinformatics))--University of Pretoria, 2020. / Geography, Geoinformatics and Meteorology / MSc (Geoinformatics) / Unrestricted

An airfoil shape optimization problem with conflicting objectives is handled using two different multi-objective approaches. These are an a priori scalarization approach where the conflicting objectives are assigned weights and summed together to form a single objective, and the Pareto-optimal multi-objective approach. The optimization formulations for both approaches contain challenging numerical characteristics which include noise, multi-modality and undefined regions. Gradient-, surrogate- and population-based single objective optimization methods are applied to the `a priori' formulations. The gradient methods are modified to improve their performance on noisy problems as well as to handle undefined regions in the design space. The modifications are successful but the modified methods are outperformed by the surrogate methods and population based methods. Population-based techniques are used for the Pareto-optimal multi-objective approach. Two established optimization algorithms and two custom algorithms are implemented. The custom algorithms use fitted unrotated hyper ellipses and linear aggregating functions to search the design space for non-dominated designs. Various multi-objective formulations are posed to investigate different aspects of the airfoil design problem. The non-dominated designs found by the Pareto-optimal multi-objective optimization algorithms are then presented. / Dissertation (MEng)--University of Pretoria, 2011. / Mechanical and Aeronautical Engineering / unrestricted

The first objective of this research project is to carry out multi-objective optimization (MOO) for four simple chemical engineering processes to clearly demonstrate the wealth of information on a given process that can be obtained from the MOO instead of a single aggregate objective function. The four optimization case studies are the design of a PI controller, an SO2 to SO3 reactor, a distillation column and an acrolein reactor. Results that were obtained from these optimization case studies show the benefit of generating and using the Pareto domain to gain a deeper understanding of the underlying relationships between the various process variables and the different performance objectives. In addition, an acrylic acid production plant model is developed in order to propose a methodology to solve multi-objective optimization for the two-reactor system model using artificial neural networks (ANNs) as metamodels, in an effort to reduce the computational time requirement that is usually very high when first-principles models are employed to approximate the Pareto domain. Once the metamodel was trained, the Pareto domain was circumscribed using a genetic algorithm and ranked with the Net Flow method (NFM). After the MOO was carry out with the ANN surrogate model, the optimization time was reduced by a factor of 15.5.

This dissertation work builds upon the theoretical and experimental studies of radio frequency micro- and nano-electromechanical systems (RF M/NEMS) integrated multifunctional reconfigurable antennas (MRAs). This work focuses on three MRAs with an emphasis on a wireless local area network (WLAN), 5-6 GHz, beam tilt, and polarization reconfigurable parasitic layer-based MRA with inset micro-strip feed. The other two antennas are an X band (8-12 GHz) beam steering MRA with aperture-coupled micro-strip fed and wireless personal area network (WPAN), 60 GHz, inset micro-strip fed MRA for dual frequency and dual polarization operations. For the WLAN (5-6 GHz) MRA, a detailed description of the design methodology, which is based on the joint utilization of electromagnetic (EM) full-wave analysis and multi-objective genetic algorithm, and fundamental theoretical background of parasitic layer-based antennas are given. Various prototypes of this MRA have been fabricated and measured. The measured and simulated results for both impedance and radiation characteristics are given. The work on the MRAs operating in the X band and 60 GHz region focuses on the theoretical aspects of the designs. Different than the WLAN MRA, which uses inset fed structure, the aperture-coupled feed mechanism has been investigated with the goal of improving the bandwidth and beam-tilt capabilities of these MRAs. The simulated results are provided and the working mechanisms are described. The results show that the aperture-coupled feed mechanism is advantageous both in terms of enhanced bandwidth and beam-steering capabilities. Finally, this dissertation work concludes with plans for future work, which will build upon the findings and the results presented herein.

Flood severity and frequency have grown over the years as a result of urban development and climate change. Floods in cities cause major challenges such as property and infrastructure damage, transportation congestion, loss of life, environmental threats, and health concerns. To relieve the load on the urban drainage system and prevent flooding, effective measures to strengthen its resilience are required. Traditional design methods, which rely on past performance trends and long lifespans, usually result in infrastructure that is inflexible and unable to adapt to changing situations. Those traditional studies focused on drainage design, such as pipe slope and diameter optimization, coupling design cost limitation. Furthermore, various terminologies for the overall concept of green/grey infrastructure have been proposed in the literature. Some studies have been focused on the optimization of the suitable locations for storage tanks, which would be one of the most efficient approaches. Building storage facilities such as retention or detention basins are a cost-effective and efficient structural option to improve the resilience of urban sewerage system, reducing peak runoff in existing drainage systems in urban areas, especially compared to traditional methodologies such as increasing pipe diameter or slope providing sufficient hydraulic capacity. The basic concept is to create an optimization framework using Non-dominated Sorting Genetic Algorithm II (NSGA II), coupling with hydraulic model SWMM, and use it to change a number of drainage system-related variables such pipe diameter, slope, and storage unit size. The main idea of the optimization framework in thesis is to combine different methods into one framework, which is a challenge in a complex system due to the dilemma between the resilience objective and financial limitation. Literature review would shows that the recent research in terms of sewerage system resilience optimization utilizing different methodologies. Application of the system would shows that optimization model has the capability to improve the resiliency of urban sewerage system. The main objective of the thesis are (i) develop a new framework to optimize volume and location of storage units in urban drainage systems; (ii) develop a two-stage multi-objective optimization framework; (iii) develop the new index to make the optimization process feasible.

In this study, two new Multi-Objective Optimization (MOO) techniques are developed. The two new techniques, the Objective-Based Gradient Algorithm (OBGA) and the Principal Component Grid Algorithm (PCGA), were developed with the goals of improving the accuracy and efficiency of the Pareto domain approximation relative to current MOO techniques. Both methods were compared to current MOO techniques using several test problems. It was found that both the OBGA and PCGA systematically produced a more accurate Pareto domain than current MOO techniques used for comparison, for all problems studied. The OBGA requires less computation time than the current MOO methods for relatively simple problems whereas for more complex objective functions, the computation time was larger. On the other hand, the efficiency of the PCGA was higher than the current MOO techniques for all problems tested. The new techniques were also applied to complex chemical engineering problems. The OBGA was applied to an industrial reactor producing ethylene oxide from ethylene. The optimization varied four of the reactor input parameters, and the selectivity, productivity and a safety factor related to the presence of oxygen in the reactor were maximized. From the optimization results, recommendations were made based on the ideal reactor operating conditions, and the control of key reactor parameters. The PCGA was applied to a PI controller model to develop new tuning methods based on the Pareto domain. The developed controller tuning methods were compared to several previously developed controller correlations. It was found that all previously developed controller correlations showed equal or worse performance than that based on the Pareto domain. The tuning methods were applied to a fourth order process and a process with a disturbance, and demonstrated excellent performance.

Water resources managers rely on information collected by hydrometric networks without a quantitative way to assess their efficiency, and most Canadian water monitoring networks still do not meet the minimum density requirements. There is also no established way to quantify the importance of each existing station in a hydrometric network. This research examines the properties of Combined Regionalization Dual Entropy Multi-Objective Optimization (CR-DEMO), a robust network design technique which combines the merits of information theory and multi-objective optimization. Another information theory based method called transinformation (TI) which can rank the contribution of unique information from each specific hydrometric station in the network is tested for use with CR-DEMO. When used in conjunction, these methods can not only provide an objective measure of network efficiency and the relative importance of each station, but also allow the user to make recommendations to improve existing hydrometric networks across Canada. The Ottawa River Basin, a major Canadian watershed in Ontario and Quebec, was selected for analysis. Various regionalization methods which could be used in CR-DEMO such as distance weighting and a rainfall runoff model were compared in a leave one out cross validation. The effect of removing stations with regulated and unnatural flow regimes from the regionalization process is also tested. The analysis is repeated on a smaller tributary of the Ottawa River Basin, the Madawaska Watershed, to examine scale effects in TI analysis and CR-DEMO application. In this study, tests were conducted to determine whether to include stations outside of the river basin in order to provide more context to the basin boundaries. It was found that the TI analysis complemented CR-DEMO well and it provided a detailed station ranking which was supported by CR-DEMO results. The inverse distance weighting drainage area ratio method was found to provide more accurate regionalization results compared to the rainfall-runoff model, and was thus chosen for CR-DEMO. Regionalization was shown to be more accurate when the regulated basins were omitted using leave one out cross validation. It was discovered that CR-DEMO is sensitive to scaling because some sub-basins which are relatively “well-equipped” compared to others in dire conditions may be penalized. The TI analysis was not as sensitive to scaling. Including stations outside of the Ottawa River Basin improved the information density and regionalization accuracy in the Madawaska Watershed because they provided context to sparse areas. Finally, Pareto optimal network solutions for both the Ottawa River Basin and the Madawaska Watershed were presented and analyzed. A number of optimal networks are proposed for each watershed along with “hot-spots” where new stations should be added whatever the end users’ choice of network. / Thesis / Master of Applied Science (MASc)