Thesis (M.S.)--University of Wisconsin--Madison, 1968. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2006. / Dr. Jerome Meisel, Committee Member ; Dr. Bill Wepfer, Committee Member ; Dr. Samuel V. Shelton, Committee Chair.

Thesis (M.Eng.)(Mechanical)--University of Pretoria, 2005. / Title form opening screen (viewed March 20 2006). Summaries in English and Afrikaans. Includes bibliographical references.

Thesis (M.S.) in Mechanical Engineering--University of Maine, 2005. / Includes vita. Includes bibliographical references (leaves 135-142).

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1978. / Vita. Abstract. Includes bibliographical references (leaves 193-197). Also available via the Internet.

Thesis (Ph. D.)--Drexel University, 2007. / Includes abstract and vita. Includes bibliographical references (leaves 173-182).

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: uniformity of heating; optimization; optimal process; modeling; microwave pulsing; microwave heating; FDTD method; coupled problem. Includes bibliographical references (leaves 50-59).

Many-objective optimization problems (MaOPs), in which the number of objectives is greater than three, are common in various applications, and have drawn many scholars' attention. Evolutionary multiobjective optimization (EMO) algorithms have been successfully applied to solve bi- and tri-objective optimization problems. However, MaOPs are more challenging compared with the bi- and tri-objective optimization problems. The performances of most existing classical EMO algorithms generally deteriorate over the number of objectives. Thus, this thesis presents a weight design method to modify classical decomposition-based EMO algorithms for solving MaOPs, and a novel objective extraction method to transform the MaOP into a problem with few objectives.;Additionally, performance metrics play an important role in understanding the strengths and weaknesses of an algorithm. To the best of our knowledge, there is no direct performance metric for the objective reduction algorithms. Their performance can only be indirectly evaluated by the metrics, such as IGD-metric and H-metric, of the solutions obtained by an EMO algorithm equipped with the objective reduction method. This thesis presents a direct performance metric featuring the simplicity and usability of the objective reduction algorithms. Meanwhile, we propose a novel framework for many-objective test problems, which features both simple and complicated Pareto set shape, and is scalable in terms of the numbers of the objectives and the essential objectives. Also, we can control the importance of essential objectives.;As some MaOPs may have redundant or correlated objectives, it is desirable to reduce the number of the objectives in such circumstances. However, the Pareto solution of the reduced problem obtained by most existing objective reduction methods may not be the Pareto solution of the original MaOP. Thus, this thesis proposes an objective extraction method for MaOPs. It formulates the reduced objective as a linear combination of the original objectives to maximize the conflict between the reduced objectives. Subsequently, the Pareto solution of the reduced problem obtained by the proposed algorithm is that of the original MaOP, and the proposed algorithm can preserve the non-dominant relation as much as possible. We compare the proposed objective extraction method with three objective reduction methods, i.e., REDGA, L-PCA and NL-MVU-PCA. The numerical studies show the effectiveness and robustness of the proposed approach.;The decomposition-based EMO algorithms, e.g. MOEA/D, M2M, have demonstrated the effectiveness in dealing with MaOPs. Nevertheless, these algorithms need to design the weight vectors, which has significant effects on the algorithms' performance. Especially, when the Pareto front of the problem is incomplete, these algorithms cannot obtain a set of uniform solutions by using the conventional weight design methods. Not only can self-organizing map (SOM) preserve the topological properties of the input data by using the neighborhood function, but also its display is more uniform than the probability density of the input data. This phenomenon is advantageous to generate a set of uniform weight vectors based on the distribution of the individuals. Therefore, we propose a novel weight design method based on SOM, which can be integrated with most of the decomposition-based EMO algorithms. In this thesis, we choose the existing M2M algorithm as an example for such integration. This integrated algorithm is then compared with the original M2M and two state-of-the-art algorithms, i.e. MOEA/D and NSGA-II on eleven redundancy problems and eight non-redundancy problems. The experimental results show the effectiveness of the proposed approach.

This dissertation addresses the issue of optimum system design to achieve reliable communication in the presence of various types of interference. Multiobjective formulation is used with noncooperative and cooperative approaches owing to the nature of the problems under consideration. Since intentional Jamming is one of the most severe kinds of interference, anti-jam techniques are crucial for communications in a hostile environment. The jam and anti-jam problem is modeled as a two-person zero-sum game in which the communicator and the jammer have antagonistic objectives and are viewed as the two players. The concept of Nash equilibrium is introduced and its characterizations such as existence, uniqueness, stability, robustness, and sensitivity are investigated. This model is then applied to a frequency-hop spread spectrum M-ary frequency-shift-keying system where ratio-threshold diversity is used to combat partial-band noise and multitone jamming. Equilibrium performance in terms of cutoff rate and bit error rate is shown to be superior to that predicted by worst-case analysis. When mutual interference caused by simultaneous transmissions is the major concern in a heterogeneous packet network, a multiobjective framework is proposed in this dissertation with the objectives and constraints of the individual users taken into consideration. Near-far effect and Rayleigh fading may occasion packet capture and therefore create unfairness in favor of closer users. Thus, multiobjective optimality is introduced, in which criterion of fairness is embedded. Optimum strategies controlling transmission probability and/or power are examined to yield the Pareto optimal solution in a slotted ALOHA network. Then, the same control strategies are studied with the channel utilization being the maximization objective. Optimization results are obtained in various situations, and effectiveness of different strategies is compared. A multimedia direct-sequence spread spectrum system may support multiple services with different transmission rates and diverse quality-of-service requirements. To facilitate multimedia applications and maximize the system capacity, average power control, error correction coding, and time diversity are incorporated into the system. The capacity of such a system is evaluated in multipath Rayleigh fading channels. Average bit error rate, outage probability, and corresponding information theoretic bounds are discussed. Concatenation of Reed-Solomon codes and convolutional codes is considered for error correction to account for different quality and delay constraints. It is shown through a numerical example that the system capacity can be increased significantly by an appropriate system design. / Graduate