This thesis presents new solution approaches for land leveling, using optimal earthmoving vehicle routing. It addresses the Shortest Route Cut and Fill Problem (SRCFP) developed by Henderson, Vaughan, Wakefield and Jacobson [2000]. The SRCFP is a discrete optimization search problem, proven to be NP-hard. The SRCFP describes the process of reshaping terrain through a series of cuts and fills. This process is commonly done when leveling land for building homes, parking lots, etc. The model used to represent this natural system is a variation of the Traveling Salesman Problem. The model is designed to limit the time needed to operate expensive, earthmoving vehicles. The model finds a vehicle route that minimizes the total time required to travel between cut and fill locations while leveling the site. An optimal route is a route requiring the least amount of travel time for an individual earthmoving vehicle. This research addresses the SRCFP by evaluating minimum function values across an unknown response surface. The result is a cost estimating strategy that provides construction planners a strategy for contouring terrain as cheaply as possible. Other applications of this research include rapid runway repair, and robotic vehicle routing. / Master of Science

We present a theory for constructing linear, black box approximations to face recognition algorithms and empirically demonstrate that a surprisingly diverse set of face recognition approaches can be approximated well using a linear model. The construction of the linear model to a face recognition algorithm involves embedding of a training set of face images constrained by the distances between them, as computed by the face recognition algorithm being approximated. We accomplish this embedding by iterative majorization, initialized by classical multi-dimensional scaling (MDS). We empirically demonstrate the adequacy of the linear model using six face recognition algorithms, spanning both template based and feature based approaches on standard face recognition benchmarks such as the Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets. The experimental results show that the average Error in Modeling for six algorithms is 6.3% at 0.001 False Acceptance Rate (FAR), for FERET fafb probe set which contains maximum number of subjects among all the probe sets. We demonstrate the usefulness of the linear model for algorithm dependent indexing of face databases and find that it results in more than 20 times reduction in face comparisons for Bayesian Intra/Extra-class person classifier (BAY), Elastic Bunch Graph Matching algorithm (EBGM), and the commercial face recognition algorithms. We also propose a novel paradigm to reconstruct face templates from match scores using the linear model and use the reconstructed templates to explore the security breach in a face recognition system. We evaluate the proposed template reconstruction scheme using three, fundamentally different, face recognition algorithms: Principal Component Analysis (PCA), Bayesian Intra/Extra-class person classifier (BAY), and a feature based commercial algorithm. With an operational point set at 1% False Acceptance Rate (FAR) and 99% True Acceptance Rate (TAR) for 1196 enrollments (FERET gallery), we show that at most 600 attempts (score computations) are required to achieve 73%, 72% and 100% chance of breaking in as a randomly chosen target subject for the commercial, BAY and PCA based face recognition system, respectively. We also show that the proposed reconstruction scheme has 47% more probability of breaking in as a randomly chosen target subject for the commercial system as compared to a hill climbing approach with the same number of attempts.

La conception des filtres a réponse impulsionnelle finie (RIF) peut être formulée comme un problème d'optimisation non linéaire réputé pour être difficile sa résolution par les approches conventionnelles. Afin d'optimiser la conception des filtres RIF, nous explorons plusieurs méthodes stochastiques capables de traiter de grands espaces. Nous proposons un nouvel algorithme génétique dans lequel certains concepts innovants sont introduits pour améliorer la convergence et rendre son utilisation plus facile pour les praticiens. Le point clé de notre approche découle de la capacité de l'algorithme génétique (AG) pour adapter les opérateurs génétiques au cours de la vie génétique tout en restant simple et facile à mettre en oeuvre. Ensuite, l’optimisation par essaim de particules (PSO) est proposée pour la conception de filtres RIF. Finalement, un algorithme génétique hybride (HGA) est proposé pour la conception de filtres numériques. L'algorithme est composé d'un processus génétique pur et d’une approche locale dédiée. Notre contribution vise à relever le défi actuel de démocratisation de l'utilisation des AG’s pour les problèmes d’optimisation. Les expériences réalisées avec différents types de filtres mettent en évidence la contribution récurrente de l'hybridation dans l'amélioration des performances et montrent également les avantages de notre proposition par rapport à d'autres approches classiques de conception de filtres et d’autres AG’s de référence dans ce domaine d'application. / The design of finite impulse response (FIR) filters can be formulated as a non-linear optimization problem reputed to be difficult for conventional approaches. In order to optimize the design of FIR filters, we explore several stochastic methodologies capable of handling large spaces. We propose a new genetic algorithm in which some innovative concepts are introduced to improve the convergence and make its use easier for practitioners. The key point of our approach stems from the capacity of the genetic algorithm (GA) to adapt the genetic operators during the genetic life while remaining simple and easy to implement. Then, the Particle Swarm Optimization (PSO) is proposed for FIR filter design. Finally, a hybrid genetic algorithm (HGA) is proposed for the design of digital filters. The algorithm is composed of a pure genetic process and a dedicated local approach. Our contribution seeks to address the current challenge of democratizing the use of GAs for real optimization problems. Experiments performed with various types of filters highlight the recurrent contribution of hybridization in improving performance. The experiments also reveal the advantages of our proposal compared to more conventional filter design approaches and some reference GAs in this field of application.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis focuses on optimization of wind power tracking control systems in order to capture maximum wind power for the generation system. In this work, a mathematical simulation model is developed for a variable speed wind turbine power generation system. The system consists a wind turbine with necessary transmission system, and a permanent magnet synchronous generator and its vector control system. A new fuzzy based hill climbing method for power tracking control is proposed and implemented to optimize the wind power for the system under various conditions. Two existing power tracking control methods, the tip speed ratio (TSR) control method and the speed sensorless control method are also implemented with the wind power system. The computer simulations with a 5 KW wind power generation system are performed. The results from the proposed control method are compared with those obtained using the two existing methods. It is illustrated that the proposed method generally outperforms the two existing methods, especially when the operating point is far away from the maximum point. The proposed control method also has similar stable characteristic when the operating point is close to the peak point in comparison with the existing methods. The proposed fuzzy control method is computationally efficient and can be easily implemented in real-time.

The Master Thesis deals with logistic module innovation of information system ERP. The principle of innovation is based on implementation of heuristic algorithms which solve Travel Salesman Problems (TSP). The software MATLAB is used for analysis and tests of these algorithms. The goal of Master Thesis is the comparison of selections algorithm, which are suitable for economic purposes (accuracy of solution, speed of calculation and memory demands).

This diploma thesis deals with evolutionary algorithms used for travelling salesman problem (TSP). In the first section, there are theoretical foundations of a graph theory and computational complexity theory. Next section contains a description of chosen optimization algorithms. The aim of the diploma thesis is to implement an application that solve TSP using evolutionary algorithms.