Aligning two images by point to point correspondence is a hard optimization problem. It can be solved using t-Extremal Optimization or with a modification of this method called Fitness threshold accepting. In this work these two methods are tested and compared to see whether one of the methods should be preferred for image alignment. Since real image data is almost always noisy the performance of the methods under conditions like noisy and outlying data is analyzed too.

Tracking control algorithms often target the convergence of a tracking error. However, this can be at the expense of other important system characteristics, such as the control effort used to annihilate the tracking error, transient response, or steady-state characteristics, for example. Furthermore, most tracking control methods assume prior knowledge of the system dynamics, which is not always a realistic assumption, especially in the case of highly complex systems. In this thesis, a model-free optimized tracking control architectural heuristic is proposed. The suggested feedback system is composed of two control loops. The first is the tracking loop. It focuses on the convergence of the tracking error. It is implemented using two different model-free control algorithms for comparison purpose: Reinforcement Learning (RL) and the Nonlinear Threshold Accepting (NLTA) technique. The RL scheme reformulates the tracking error combinations into a form of Markov-Decision-Process (MDP) and applies Q-Learning to build the best tracking control policy for the dynamic system under consideration. On the other hand, the NLTA algorithm is applied to tune the gains of a PID controller. The second control loop is in the form of a nonlinear state feedback loop. It is implemented using a feedforward artificial neural network (ANN) to optimize a system-wide cost function which can be flexible enough to encompass a set of desired design requirements pertaining to the targeted system behavior. This may include, for instance, the target overshoot, settling time, rise time, etc. The proposed architectural heuristic provides a model-free framework to tackle such control problems, in the sense that the plant's dynamic model is not required to be known in advance. Yet, at least a subset of the stability region of the optimized gains has to be known in advance so that it can provide a search space for the optimization algorithms. Simulation results on two dynamic systems demonstrate the superiority of the proposed control scheme.

In this study we focus on two-sided mixed-model assembly line balancing type-I problem. There is a production target for a fixed time horizon and the objective is to produce this amount with the minimum level of workforce. A mathematical model is developed to solve this problem in an optimal manner. For large scale problems, the mathematical model fails to give the optimal solution within reasonable computational times. Thus, a heuristic approach based on threshold accepting algorithm is presented. Both the mathematical model and the heuristic approach are executed to solve several example problems from the literature and a case study problem which is derived from the refrigerator production. Computational experiments are carried out using both approaches. It is observed that the heuristic procedure finds good solutions within very reasonable computational times.

Aligning two images by point to point correspondence is a hard optimization problem. It can be solved using t-Extremal Optimization or with a modification of this method called Fitness threshold accepting. In this work these two methods are tested and compared to see whether one of the methods should be preferred for image alignment. Since real image data is almost always noisy the performance of the methods under conditions like noisy and outlying data is analyzed too.

In this thesis random walks similar to the Metropolis algorithm are investigated. Special emphasis is laid on different types of acceptance probabilities, namely Metropolis, Tsallis and Threshold Accepting. Equilibrium and relaxation properties as well as performance aspects in stochastic optimization are investigated. Analytical investigation of a simple system mimicking an harmonic oscillator yields that a variety of acceptance probabilities, including the abovementioned, result in an equilibrium distribution that is widely dominated by an exponential function. In the last chapter an optimal optimization schedule for the Tsallis acceptance probability for the idealized barrier is investigated. / In dieser Dissertation werden Random Walks ähnlich dem Metropolis Algorithmus untersucht. Es werden verschiedene Akzeptanzwahrscheinlichkeiten untersucht, dabei werden Metropolis, Tsallis und Threshold Accepting besonders betrachtet. Gleichgewichts- und Relaxationseigenschaften sowie Performanceaspekte im Bereich der stochastischen Optimierung werden untersucht. Die Analytische Betrachtung eines simplen, dem harmonischen Oszillator ähnlichen Systems zeigt, dass eine Reihe von Akzeptanzwahrscheinlichkeiten, eingeschlossen die oben Erwähnten, eine Gleichgewichtsverteilung ausbilden, die von einer Exponentialfunktion dominiert wird. Im letzten Kapitel wird der optimale Schedule für die Tsallis Akzeptanzwahrscheinlichkeit für eine idealisierte Barriere untersucht.

In this thesis random walks similar to the Metropolis algorithm are investigated. Special emphasis is laid on different types of acceptance probabilities, namely Metropolis, Tsallis and Threshold Accepting. Equilibrium and relaxation properties as well as performance aspects in stochastic optimization are investigated. Analytical investigation of a simple system mimicking an harmonic oscillator yields that a variety of acceptance probabilities, including the abovementioned, result in an equilibrium distribution that is widely dominated by an exponential function. In the last chapter an optimal optimization schedule for the Tsallis acceptance probability for the idealized barrier is investigated. / In dieser Dissertation werden Random Walks ähnlich dem Metropolis Algorithmus untersucht. Es werden verschiedene Akzeptanzwahrscheinlichkeiten untersucht, dabei werden Metropolis, Tsallis und Threshold Accepting besonders betrachtet. Gleichgewichts- und Relaxationseigenschaften sowie Performanceaspekte im Bereich der stochastischen Optimierung werden untersucht. Die Analytische Betrachtung eines simplen, dem harmonischen Oszillator ähnlichen Systems zeigt, dass eine Reihe von Akzeptanzwahrscheinlichkeiten, eingeschlossen die oben Erwähnten, eine Gleichgewichtsverteilung ausbilden, die von einer Exponentialfunktion dominiert wird. Im letzten Kapitel wird der optimale Schedule für die Tsallis Akzeptanzwahrscheinlichkeit für eine idealisierte Barriere untersucht.