Optimal control methods are applied to mechanical models in order to predict the control strategies in human arm movements. Optimality criteria are used to determine unique controls for a biomechanical model of the human upper-limb with redundant actuators. The motivation for this thesis is to provide a non-task-specific method of motion prediction as a tool for movement researchers and for controlling human models within virtual prototyping environments. The current strategy is based on determining the muscle activation levels (control signals) necessary to perform a task that optimizes several physical determinants of the model such as muscular and joint stresses, as well as performance timing. Currently, the initial and final location, orientation, and velocity of the hand define the desired task. Several models of the human arm were generated using a graph-theoretical method in order to take advantage of similar system topology through the evolution of arm models. Within this framework, muscles were modelled as non-linear actuator components acting between origin and insertion points on rigid body segments. Activation levels of the muscle actuators are considered the control inputs to the arm model. Optimization of the activation levels is performed via a hybrid genetic algorithm (GA) and a sequential quadratic programming (SQP) technique, which provides a globally optimal solution without sacrificing numerical precision, unlike traditional genetic algorithms. Advantages of the underlying genetic algorithm approach are that it does not require any prior knowledge of what might be a 'good' approximation in order for the method to converge, and it enables several objectives to be included in the evaluation of the fitness function. Results indicate that this approach can predict optimal strategies when compared to benchmark minimum-time maneuvers of a robot manipulator. The formulation and integration of the aforementioned components into a working model and the simulation of reaching and lifting tasks represents the bulk of the thesis. Results are compared to motion data collected in the laboratory from a test subject performing the same tasks. Discrepancies in the results are primarily due to model fidelity. However, more complex models are not evaluated due to the additional computational time required. The theoretical approach provides an excellent foundation, but further work is required to increase the computational efficiency of the numerical implementation before proceeding to more complex models.

This thesis is based on Gordon-Schaefer model, and assesses Argentine shortfin squid¡¦s stock by using the data of Southwest Atlantic from FAO between 1983 and 2009. First, estimate the equilibrium level of the open-access fishery and dynamic optimization fishery and compare to each other. Then estimate annual Argentine shortfin squid¡¦s stock size, comparing the stock size with the equilibrium level of the two fishery models. The result is that Argentine shortfin squid¡¦s stock size has no crisis of extinction now in Southwest Atlantic. In addition, simulate Argentine shortfin squid¡¦s stock size under management and no management status in the future. The result is that it will make the Argentine shortfin squid sustainable development under dynamic optimization fishery, and this fishery model will be a good management. Finally, this thesis based on the catch of Southwest Atlantic Argentine shortfin squid, which we figure out the fluctuation of catch by literatures, and do the sensitivity analysis.

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Sergestid Shrimp contains rich nutrition, regards as a high-class aquatic product in Japan. The management of the catch has come into operation, and it¡¦s led the price raising and the output value increasing rapidly when the establishment of TungKung producer organization of the Sergestid Shrimp in 1993 and it also has become the important seasonal fishery in the southwestern coast of Taiwan. This study is based on the fundamental model of fish dynamic- Gordon Schaefer Model, to discuss the equilibrium values for the optimal conduction of open access and dynamic optimization, and to do the comparative statics analysis. By applying the data provided by Fisheries Research Institute, the evaluation of the variation are under both conductions were available, and in additions, the sensitivity analysis had been done by assuming all bio-economics parameters varied within a reasonable range. The study can figure out the fact that the management of TungKang producer organization of the Sergestid Shrimp with the notion of the sustainable administration by the derivation of theoretical model and the simulate analysis of historical data, and the conclusions of analysis are consistency. Furthermore, the study discusses the fishery management policies of TungKung Sergestid Shrimp. I hope the management policies of TungKung producer organization of the Sergestid Shrimp could be popularized in the related industry.

This dissertation focuses on the economic and environmental benefits of planting switchgrass as a bioenergy feedstock. The first chapter presents a dynamic optimization model of fertilizer and land allocation between switchgrass and corn to estimate economic benefits. Subsequent chapters utilize Geographic Information System (GIS)-based Soil and Water Assessment Tool (SWAT) to be calibrated to evaluate the environmental (nutrient and sediment loading) effects of land use conversion to switchgrass production on water quality and analyze the Water Quality Trading (WQT) program with cost-effectiveness ratios ordered for abatements of nutrient loadings in an East Tennessee watershed.

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This dissertation investigates three economic aspects of the climate change issue: optimal allocation of investment between adaptation and mitigation, impacts on a ground water dependent regional agricultural economy and effects on global food insecurity. This is done in three essays by applying mathematical programming. In the first essay, a modeling study is done on optimal temporal investment between climate change adaptation and mitigation considering their relative contributions to damage reduction and diversion of funds from consumption and other investments. To conduct this research, we extend the widely used Integrated Assessment Model?DICE (Dynamic Integrated Climate Economy) adding improved adaptation modeling. The model results suggest that the joint implementation of adaptation and mitigation is welfare improving with a greater immediate role for adaptation. In the second essay, the research focuses on the ground water dependent agricultural economy in the Texas High Plains Region. A regionally detailed dynamic land allocation model is developed and applied for studying interrelationships between limited natural resources (e.g. land and groundwater), climate change, bioenergy demands and agricultural production. We find out that the effect varies regionally across hydrologically heterogeneous regions. Also, water availability has a substantial impact on feedstock mix. In terms of biofuel feedstock production, the model results show that limited water resource cannot sustain expanded corn-based ethanol production in the future. In the third essay, a Computable General Equilibrium (CGE) model is applied in an attempt to study potential impacts of climate change on global food insecurity. Our results show that climate change alters the number of food insecure people in a regionally different fashion over time. In general, the largest increase of additional food insecure population relative to the reference case (no climate change) is found in Africa and South Asia, while most of developed countries will benefit from climate change with a reduced proportion of food insecure population. In general, climate change affects world agricultural production and food security. Integrated adaptation and mitigation strategy is more effective in reducing climate change damages. However, there are synergies/trade-offs between these two options, particularly in regions with limited natural resources.

Machine emulation allows for the simulation of a real or virtual machine, the source machine, on various host computers. A machine emulator interprets programs that are compiled for the emulated machine, but normally at a much reduced speed. Therefore, in order to increase the executions peed of such interpreted programs, a machine emulator may apply different dynamic optimization techniques. In our research we focus on emulators for real machines, i.e. existing computer architectures, and in particular on dynamic binary translation as the optimization technique. With dynamic binary translation, the machine instructions of the interpreted source program are translated in to machine instructions for the host machine during the interpretation of the program. Both, the machine emulator and its dynamic binary translator a resource and host machine specific, respectively, and are therefore traditionally hand-written. In this thesis we introduce the Walkabout/Yirr-Ma framework. Walkabout, initially developed by Sun Micro systems, allows among other things for the generation of instrumented machine emulators from a certain type of machine specification files. We extended Walkabout with our generic dynamic optimization framework ‘Yirr-Ma’ which defines an interface for the implementation of various dynamic optimizers: by instrumenting a Walkabout emulator’s instruction interpretation functions, Yirr-Ma observes and intercepts the interpretation of a source machine program, and applies dynamic optimizations to selected traces of interpreted instructions on demand. One instance of Yirr-Ma’s interface for dynamic optimizers implements our specification-driven dynamic binary translator, the major contribution of this thesis. At first we establish two things: a formal framework that describes the process of machine emulation by abstracting from real machines, and different classes of applicable dynamic optimizations. We define dynamic optimizations by a set of functions over the abstracted machine, and dynamic binary translation as one particular optimization function. Using this formalism, we then derive the upper bound for quality of dynamically translated machine instructions. Yirr-Ma’s dynamic binary translator implements the optimization functions of our formal framework by modules which are either generated from, or parameterized by, machine specification files. They thus allow for the adaptation of the dynamic binary translator to different source and host machines without hand-writing machine dependent code.

Closed-loop optimization deals with problems in which candidate solutions are evaluated by conducting experiments, e.g. physical or biochemical experiments. Although this form of optimization is becoming more popular across the sciences, it may be subject to rather unexplored resourcing issues, as any experiment may require resources in order to be conducted. In this thesis we are concerned with understanding how evolutionary search is affected by three particular resourcing issues -- ephemeral resource constraints (ERCs), changes of variables, and lethal environments -- and the development of search strategies to combat these issues. The thesis makes three broad contributions. First, we motivate and formally define the resourcing issues considered. Here, concrete examples in a range of applications are given. Secondly, we theoretically and empirically investigate the effect of the resourcing issues considered on evolutionary search. This investigation reveals that resourcing issues affect optimization in general, and that clear patterns emerge relating specific properties of the different resourcing issues to performance effects. Thirdly, we develop and analyze various search strategies augmented on an evolutionary algorithm (EA) for coping with resourcing issues. To cope specifically with ERCs, we develop several static constraint-handling strategies, and investigate the application of reinforcement learning techniques to learn when to switch between these static strategies during an optimization process. We also develop several online resource-purchasing strategies to cope with ERCs that leave the arrangement of resources to the hands of the optimizer. For problems subject to changes of variables relating to the resources, we find that knowing which variables are changed provides an optimizer with valuable information, which we exploit using a novel dynamic strategy. Finally, for lethal environments, where visiting parts of the search space can cause the permanent loss of resources, we observe that a standard EA's population may be reduced in size rapidly, complicating the search for innovative solutions. To cope with such scenarios, we consider some non-standard EA setups that are able to innovate genetically whilst simultaneously mitigating risks to the evolving population.

A novel Ant Colony Optimization (ACO) framework for a dynamic environment has been proposed in this study. This algorithm was developed to solve Dynamic Traveling Salesman Problems more efficiently than the current algorithms. Adaptive Large Neighborhood Search based immigrant schemes have been developed and compared with existing ACO-based immigrant schemes in literature to maintain diversity via transferring knowledge to the pheromone trails from previous environments. Numerical results indicate that the proposed algorithm can handle dynamicity in the environment more efficiently compared to other immigrant-based ACOs available in the literature. A real-life case study for wildlife surveillance by unmanned aerial vehicles has also been developed and solved using the proposed algorithm.