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Electric motors are used more widely in automotive to reducing emissions in vehicles. Due to the decreased usage of internal combustion engines which used to be the main noise source, impacts from synchronizers cannot be ignored during gear shifting, not only causing noise and wear but also delaying gear shifting completion.  To minimize the impacts during gear shifting, a dog teeth position sensor is required but the high calculation frequency leads to a high cost, due to the high velocity of synchronizer portions and the dog teeth number.  In this thesis, the gear shifting transmission is being modelled, in order to study the process of gear shifting and engagement. The transmission model, which is expressed with electrics and dynamics formulations. In order to avoid the impact without the dog teeth position sensor, this thesis proposes an estimation algorithm based on the transmission model to approve the gear engagement if the first and second portions of synchronizers are engaged in the mating position without impacts.  Two different learning algorithms: direct comparison and particle swarm optimization application, are presented in the thesis as well, which are used to calibrate a parameter in the off-time test as part of the end of the calibration line, the so-called relevant initial phase being used in the real-time estimation.  The transmission model is simulated in Simulink and different algorithms are running in MATLAB. All these results are plotted and analyzed for further evaluation in different aspects in the result chapter. The direct comparison algorithm has a simpler structure of computation but the quantity of required actuation is uncertain in this algorithm with a probability of failure to find the solution. The application of particle swarm optimization in this case succeeds in calibrating the objective parameter with a small error than the other algorithm. Actuation quantity affects the accuracy of the solutions and errors but not the failure rate. / Elektriska motorer används i allt större utsträckning inom fordonsindustrin för att minska utsläppen från fordon. Den minskade användningen av förbränningsmotorer, som tidigare varit den främsta bullerkällan, gör att kollisioner från synkroniserare inte kan bli ignorerade under växlingen. Dessa kollisioner orsakar inte bara buller och nötningar utan även fördröjer slutförandet av växlingen.  För att minimera kollisioner under växlingen krävs det en positionssensor för dog-teeth, men den höga beräkningsfrekvensen leder till hög kostnad på grund av den höga hastigheten hos synkroniseringsdelarna samt antalet dog-teeth.  I den här avhandlingen görs en modell av växellåda för att studera växlingsprocessen och kugghjulsingreppet. Transmissionsmodellen uttrycks med elektriska och dynamiska formuleringar. För att undvika kollisioner utan positionssensor för dog-teeth, föreslås det en uppskattningsalgoritm baserad på transmissionsmodellen för att godta kugghjulsingreppet om den första and andra delen av synkroniseraren är inkopplade i parningsläget utan kollisioner.  Två olika inlärningsalgoritmer, direkt jämförelsemetoden och partikelsvärmoptimeringsmetoden presenteras även i avhandlingen. De används för att kalibrera en parameter i off-time test som en del av slutet av produktionslinjen. Denna parameter kallas för den relevanta initialfasen och används vid realtidsuppskattningen.  Transmissionsmodellen är simulerad i Simulink och de olika algoritmerna exekveras i Matlab. Alla resultat är plottade och analyserade för vidare utvärdering av olika aspekter i resultatkapitlet. Den direkta jämförelsealgoritmen har en enklare beräkningsstruktur, men mängden av nödvändig exekveringar är oklar för denna algoritm med en sannolikhet att det inte går att hitta lösningen. Däremot visar det sig att partikelsvärmoptimeringsmetoden lyckas med att kalibrera målparametern med dessutom ge mindre fel än den andra algoritmen. Antalet exekveringar påverkar lösningen samt noggrannheten hos lösningarna men påverkar inte själva felfrekvensen.

The test process for semiconductor devices involves generation and application of test patterns, failure logging and diagnosis. Traditionally, most of these activities cater for all possible faults without making any assumptions about the actual defects present in the circuit. As the size of the circuits continues to increase (following the Moore's Law) the size of the test sets is also increasing exponentially. It follows that the cost of testing has already surpassed that of design and fabrication. The central idea of our work in this dissertation is that we can have substantial savings in the test cost if we bring the actual hardware under test inside the test process's various loops -- in particular: failure logging, diagnostic pattern generation and diagnosis. Our first work, which we describe in Chapter 3, applies this idea to failure logging. We modify the existing failure logging process that logs only the first few failure observations to an intelligent one that logs failures on the basis of their usefulness for diagnosis. To enable the intelligent logging, we propose some lightweight metrics that can be computed in real-time to grade the diagnosibility of the observed failures. On the basis of this grading, we select the failures to be logged dynamically according to the actual defects in the circuit under test. This means that the failures may be logged in a different manner for devices having different defects. This is in contrast with the existing method that has the same logging scheme for all failing devices. With the failing devices in the loop, we are able to optimize the failure log in accordance with every particular failing device thereby improving the quality of diagnosis subsequently. In Chapter 4, we investigate the most lightweight of these metrics for failure log optimization for the diagnosis of multiple simultaneous faults and provide the results of our experiments. Often, in spite of exploiting the entire potential of a test set, we might not be able to meet our diagnosis goals. This is because the manufacturing tests are generated to meet the fault coverage goals using as fewer tests as possible. In other words, they are optimized for `detection count' and `test time' and not for `diagnosis'. In our second work, we leverage realtime measures of diagnosibility, similar to the ones that were used for failure log optimization, to generate additional diagnostic patterns. These additional patterns help diagnose the existing failures beyond the power of existing tests. Again, since the failing device is inside the test generation loop, we obtain highly specific tests for each failing device that are optimized for its diagnosis. Using our proposed framework, we are able to diagnose devices better and faster than the state of the art industrial tools. Chapter 5 provides a detailed description of this method. Our third work extends the hardware-in-the-loop framework to the diagnosis of scan chains. In this method, we define a different metric that is applicable to scan chain diagnosis. Again, this method provides additional tests that are specific to the diagnosis of the particular scan chain defects in individual devices. We achieve two further advantages in this approach as compared to the online diagnostic pattern generator for logic diagnosis. Firstly, we do not need a known good device for generating or knowing the good response and secondly, besides the generation of additional tests, we also perform the final diagnosis online i.e. on the tester during test application. We explain this in detail in Chapter 6. In our research, we observe that feedback from a device is very useful for enhancing the quality of root-cause investigations of the failures in its logic and test circuitry i.e. the scan chains. This leads to the question whether some primitive signals from the devices can be indicative of the fault coverage of the applied tests. In other words, can we estimate the fault coverage without the costly activities of fault modeling and simulation? By conducting further research into this problem, we found that the entropy measurements at the circuit outputs do indeed have a high correlation with the fault coverage and can also be used to estimate it with a good accuracy. We find that these predictions are accurate not only for random tests but also for the high coverage ATPG generated tests. We present the details of our fourth contribution in Chapter 7. This work is of significant importance because it suggests that high coverage tests can be learned by continuously applying random test patterns to the hardware and using the measured entropy as a reward function. We believe that this lays down a foundation for further research into gate-level sequential test generation, which is currently intractable for industrial scale circuits with the existing techniques. / Ph. D. / When a new microchip fabrication technology is introduced, the manufacturing is far from perfect. A lot of work goes into updating the fabrication rules and microchip designs before we get a higher proportion of good or defect-free chips. With continued advancements in the fabrication technology, this enhancement work has become increasingly difficult. This is primarily because of the sheer number of transistors that can be fabricated on a single chip, which has practically doubled every two years for the last four decades. The microchip testing process involves application of stimuli and checking the responses. These stimuli cater for a huge number of possible defects inside the chips. With the increase in the number of transistors, covering all possible defects is becoming practically impossible within the business constraints. This research proposes a solution to this problem, which is to make various activities in this process adaptive to the actual defects in the chips. The stimuli, we mentioned above, now depend upon the feedback from the chip. By utilizing this feedback, we have demonstrated significant improvements in three primary activities namely failure logging, scan testing and scan chain diagnosis over state-of-the-art industrial tools. These activities are essential steps related to improving the proportion of good chips in the manufactured lot.

This research presents a Hybrid Particle Swarm Optimization with Bat Algorithm (HPSOBA) based approach to solve Optimal Reactive Power Dispatch (ORPD) problem. The primary objective of this project is minimization of the active power transmission losses by optimally setting the control variables within their limits and at the same time making sure that the equality and inequality constraints are not violated. Particle Swarm Optimization (PSO) and Bat Algorithm (BA) algorithms which are nature-inspired algorithms have become potential options to solving very difficult optimization problems like ORPD. Although PSO requires high computational time, it converges quickly; while BA requires less computational time and has the ability of switching automatically from exploration to exploitation when the optimality is imminent. This research integrated the respective advantages of PSO and BA algorithms to form a hybrid tool denoted as HPSOBA algorithm. HPSOBA combines the fast convergence ability of PSO with the less computation time ability of BA algorithm to get a better optimal solution by incorporating the BA’s frequency into the PSO velocity equation in order to control the pace. The HPSOBA, PSO and BA algorithms were implemented using MATLAB programming language and tested on three (3) benchmark test functions (Griewank, Rastrigin and Schwefel) and on IEEE 30- and 118-bus test systems to solve for ORPD without DG unit. A modified IEEE 30-bus test system was further used to validate the proposed hybrid algorithm to solve for optimal placement of DG unit for active power transmission line loss minimization. By comparison, HPSOBA algorithm results proved to be superior to those of the PSO and BA methods. In order to check if there will be a further improvement on the performance of the HPSOBA, the HPSOBA was further modified by embedding three new modifications to form a modified Hybrid approach denoted as MHPSOBA. This MHPSOBA was validated using IEEE 30-bus test system to solve ORPD problem and the results show that the HPSOBA algorithm outperforms the modified version (MHPSOBA). / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

Doctor of Philosophy / Department of Electrical and Computer Engineering / Sanjoy Das / Stochastic algorithms are widely used in various modeling and optimization problems. Evolutionary algorithms are one class of population-based stochastic approaches that are inspired from Darwinian evolutionary theory. A population of candidate solutions is initialized at the first generation of the algorithm. Two variation operators, crossover and mutation, that mimic the real world evolutionary process, are applied on the population to produce new solutions from old ones. Selection based on the concept of survival of the fittest is used to preserve parent solutions for next generation. Examples of such algorithms include genetic algorithm (GA) and genetic programming (GP). Nevertheless, other stochastic algorithms may be inspired from animals’ behavior such as particle swarm optimization (PSO), which imitates the cooperation of a flock of birds. In addition, stochastic algorithms are able to address multi-objective optimization problems by using the concept of dominance. Accordingly, a set of solutions that do not dominate each other will be obtained, instead of just one best solution. This thesis proposes a multi-objective GP-PSO hybrid algorithm to recover gene regulatory network models that take environmental data as stimulus input. The algorithm infers a model based on both phenotypic and gene expression data. The proposed approach is able to simultaneously infer network structures and estimate their associated parameters, instead of doing one or the other iteratively as other algorithms need to. In addition, a non-dominated sorting approach and an adaptive histogram method based on the hypergrid strategy are adopted to address ‘convergence’ and ‘diversity’ issues in multi-objective optimization. Gene network models obtained from the proposed algorithm are compared to a synthetic network, which mimics key features of Arabidopsis flowering control system, visually and numerically. Data predicted by the model are compared to synthetic data, to verify that they are able to closely approximate the available phenotypic and gene expression data. At the end of this thesis, a novel breeding strategy, termed network assisted selection, is proposed as an extension of our hybrid approach and application of obtained models for plant breeding. Breeding simulations based on network assisted selection are compared to one common breeding strategy, marker assisted selection. The results show that NAS is better both in terms of breeding speed and final phenotypic level.

In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising 'total availability'. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis. In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising 'total availability'. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis.

隨著服務體驗經濟時代的來臨，服務提供商所面臨到的環境是愈來愈競爭及逐漸是轉由消費者所主導的型式，因此服務提供商如何去滿足顧客的需求並且達成更高的滿意度便是主要的目標之一。特別是在發展型服務提供者的環境下，他們需要去考量自已本身較不充足的服務能力及資源來設計及提供服務給顧客，而這樣的條件之下，他們也很難在短時間及時的去改善並且提供穩定的服務品質。因此，本研究提出一套是基於顧客期望理論及粒子群演算法的架構下所發展的協同式互動設計機制，希望協助發展型服務提供商解決他們所面臨的問題及創造出更大的服務價值。 本研究將協同式互動設計機制應用在會展產業的服務環境底下，並利用模擬實驗的方式去驗證此機制的有效性及鞏固性。協同式互動設計機制共有四大模組：(1)顧客偏好識別模組 (2) 粒子群期望因子選擇模組 (3) 情境式旅程抉擇模組 及(4) 服務執行模組。本研究設計此機制時考量了加入顧客間互動的能力來幫助發展型服務提供商進行更有效的服務互動並且執有效的顧客群體期望控制的目標，以便在減輕服務提供商所付出的成本之下，還能達成良好的顧客滿意度。而本研究的研究貢獻為幫助發展型服務提供商解決他們所面臨的挑戰，並且在有限的資源和能力底下，仍然可以使得他們保持與高能力服務廠商之間的競爭;而另一貢獻為在整體的服務環境底下，能讓所有的參與角色都能夠得到最大的價值，而形成一個高效能的服務生態系統。 / With the progressive advancement of the technology and fiercely-competitive environment in recent years, customers have paid more attention to the issue that how diversity and rich the service experience could satisfy their needs; in other words, the service providers must acquire the competitive advantage among other service competitors by pondering on that how to deliver the qualified service offerings in every service encounter to achieve the objective of customer satisfaction. On the other hand, many research findings noted that customers’ service quality evaluation in service encounter were influenced by the comparison between the customer expectation toward service and the service performance that they perceived; therefore, managing the customer expectation becomes the vital part concerning the customer satisfaction. Furthermore, the shortcomings of the low-moderate competence service providers is that they could not provide the constant qualified service offerings to customers in each service interaction in terms of the reason for lesser service capability and resource. Consequently, this study propose the collaborative interaction design approach which based on the Particle Swarm Optimization(PSO) algorithm to generate the dynamical service interaction among the service providers and customers for the low-moderate competence service providers and aids them to control their group customers’ expectation by collaborating with customers; in other words, the service effort of the service provider could be lightened by engaging the customer capability and the service offerings could be enhanced to provider for customers. Therefore, this study utilizes the four modules in the research framework to achieve the aforementioned objective. Ultimately, the expected contributions of this study are two-folds: (1) Aid the low-moderate competence service providers to improve the service experience for customers on the restriction of lesser service capability. (2) Utilize the PSO algorithm to decide the determinants that effectively influence on customers’ expectation considering the whole benefits among stakeholders. Hence, the collaborative interaction design proposed in this study has conspicuous benefits for the low-moderate competence service providers to preserve the competitive advantage by providing the well-design exemplar to let them follow.

The objectives of this work were to investigate, design and implement beam steering antenna arrays for mobile and wireless applications using the genetic algorithm (GA) and particle swarm optimisation (PSO) techniques as optimisation design tools. Several antenna designs were implemented and tested: initially, a printed dipole antenna integrated with a duplex RF switch used for mobile base station antenna beam steering was investigated. A coplanar waveguide (CPW) to coplanar strip (CPS) transition was adopted to feed the printed dipole. A novel RF switch circuit, used to control the RF signal fed to the dipole antenna and placed directly before it, was proposed. The measured performance of the RF switch was tested and the results confirmed its viability. Then two hybrid coupled PIN diode phase shifters, using Branchline and Rat-Race ring coupler structures, were designed and tested. The generation of four distinct phase shifts was implemented and studied. The variations of the scattering parameters were found to be realistic, with an acceptable ±2 phase shift tolerance. Next, antenna beam steering was achieved by implementing RF switches with ON or OFF mode functions to excite the radiating elements of the antenna array. The switching control process was implemented using a genetic algorithm (GA) method, subject to scalar and binary genes. Anti-phase feeding of radiating elements was also investigated. A ring antenna array with reflectors was modelled and analysed. An antenna of this type for mobile base stations was designed and simulation results are presented. Following this, a novel concept for simple beam steering using a uniform antenna array operated at 2.4 GHz was designed using GA. The antenna is fed by a single RF input source and the steering elements are reactively tuned by varactor diodes in series with small inductors. The beam-control procedure was derived through the use of a genetic algorithm based on adjusting the required reactance values to obtain the optimum solution as indicated by the cost function. The GA was also initially used as an optimisation tool to derive the antenna design from its specification. Finally, reactive loading and time modulated switching techniques are applied to steer the beam of a circular uniformly spaced antenna array having a source element at its centre. Genetic algorithm (GA) and particle swarm optimisation (PSO) processes calculate the optimal values of reactances loading the parasitic elements, for which the gain can be optimised in a desired direction. For time modulated switching, GA and PSO also determine the optimal on and off times of the parasitic elements for which the difference in currents induced optimises the gain and steering of the beam in a desired direction. These methods were demonstrated by investigating a vertically polarised antenna configuration. A prototype antenna was constructed and experimental results compared with the simulations. Results showed that near optimal solutions for gain optimisation, sidelobe level reduction and beam steering are achievable by utilising these methods. In addition, a simple switching process is employed to steer the beam of a horizontally polarised circular antenna array. A time modulated switching process is applied through Genetic Algorithm optimisation. Several model examples illustrate the radiation beams and the switching time process of each element in the array.

Detecting abnormal operating conditions, which will lead to faults developing later, has important economic implications for industries trying to meet their performance and production goals. It is unacceptable to wait for failures that have potential safety, environmental and financial consequences. Moving from a “reactive” strategy to a “proactive” strategy can improve critical equipment reliability and availability while constraining maintenance costs, reducing production deferrals, decreasing the need for spare parts. Once the fault initiates, predicting its progression and deterioration can enable timely interventions without risk to personnel safety or to equipment integrity. This work presents an online-integrated condition monitoring and prognostics framework that addresses the above issues holistically. The proposed framework aligns fully with ISO 17359:2011 and derives from the I-P and P-F curve. Depending upon the running state of machine with respect to its I-P and P-F curve an algorithm will do one of the following: (1) Predict the ideal behaviour and any departure from the normal operating envelope using a combination of Evolving Clustering Method (ECM), a normalised fuzzy weighted distance and tracking signal method. (2) Identify the cause of the departure through an automated diagnostics system using a modified version of ECM for classification. (3) Predict the short-term progression of fault using a modified version of the Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), called here MDENFIS and a tracking signal method. (4) Predict the long term progression of fault (Prognostics) using a combination of Autoregressive Integrated Moving Average (ARIMA)- Empirical Mode Decomposition (EMD) for predicting the future input values and MDENFIS for predicting the long term progression of fault (output). The proposed model was tested and compared against other models in the literature using benchmarks and field data. This work demonstrates four noticeable improvements over previous methods: (1) Enhanced testing prediction accuracy, (2) comparable processing time if not better, (3) the ability to detect sudden changes in the process and finally (4) the ability to identify and isolate the problem source with high accuracy.

Master of Science / Department of Civil Engineering / David R. Steward / Accurate modeling of the High Plains Aquifer depends on the availability of good data that represents and quantities properties and processes occurring within the aquifer. Thanks to many previous studies there is a wealth of good data available for the High Plains Aquifer but one key component, groundwater-surface water interaction locations and rates, is generally missing. Without these values accurate modeling of the High Plains Aquifer is very difficult to achieve. This thesis presents methods for simplifying the modeling of the High Plains Aquifer using a sloping base method and then applying mathematical optimization techniques to locate and quantify points of groundwater-surface water interaction. The High Plains Aquifer has a base that slopes gently from west to east and is approximated using a one-dimensional stepping base model. The model was run under steady-state predevelopment conditions using readily available GIS data representing aquifer properties such as hydraulic conductivity, bedrock elevation, recharge, and the predevelopment water level. The Levenberg-Marquardt and particle swarm algorithms were implemented to minimize error in the model. The algorithms reduced model error by finding locations in the aquifer of potential groundwater-surface water interaction and then determining the rate of groundwater to surface water exchange at those points that allowed for the best match between the measured predevelopment water level and the simulated water level. Results from the model indicate that groundwater-surface water interaction plays an important role in the overall water balance in the High Plains Aquifer. Findings from the model show strong groundwater-surface water interaction occurring in the northern basin of the aquifer where the water table is relatively shallow and there are many surface water features. In the central and southern basins the interaction is primarily limited to river valleys. Most rivers have baseflow that is a net sink from groundwater.