Title of dissertation: ENERGY EFFICIENCY OPTIMIZATION IN GREEN WIRELESS COMMUNICATIONS Feng Han, Doctor of Philosophy, 2013 Dissertation directed by: Professor K. J. Ray Liu Department of Electrical and Computer Engineering The rising energy concern and the ubiquity of energy-consuming wireless applications have sparked a keen interest in the development and deployment of energyefficient and ec...

Many real-world optimization problems have several, usually conflicting objectives. Evolutionary multi-objective optimization usually solves this predicament by searching for the whole Pareto-optimal front of solutions, and relies on a decision maker to finally select a single solution. However, in particular if the number of objectives is large, the number of Pareto-optimal solutions may be hu...

A method for incorporating fuzzy preferences into evolutionary multiobjective optimization is proposed. After introducing three commonly used models for describing fuzzy preferences, a method to convert fuzzy preferences into realvalued weight intervals is suggested. It is argued that to convert fuzzy preferences into interval-based weights is more consistent with the motivation of using fuzzy ...

We propose a general methodology for approximating the Pareto front of multi-criteria optimization problems. Our search-based methodology consists of submitting queries to a constraint solver. Hence, in addition to a set of solutions, we can guarantee bounds on the distance to the actual Pareto front and use this distance to guide the search. Our implementation, which computes and updates the d...

Random projection has been widely used in data classification. It maps high-dimensional data into a low-dimensional subspace in order to reduce the computational cost in solving the related optimization problem. While previous studies are focused on analyzing the classification performance of using random projection, in this work, we consider the recovery problem, i.e., how to accurately recove...

Recent works in evolutionary multiobjective optimization suggest to shift the focus from solely evaluating optimization success in the objective space to also taking the decision space into account. They indicate that this may be a) necessary to express the users requirements of obtaining distinct solutions (distinct Pareto set parts or subsets) of similar quality (comparable locations on the P...

We introduce and analyze a novel scalarization technique and an associated algorithm for generating an approximation of the Pareto front (i.e., the efficient set) of nonlinear multiobjective optimization problems. Our approach is applicable to nonconvex problems, in particular to those with disconnected Pareto fronts and disconnected domains (i.e., disconnected feasible sets). We establish the ...

Ensemble learning is among the state-of-the-art learning techniques, which trains and combines many base learners. Ensemble pruning removes some of the base learners of an ensemble, and has been shown to be able to further improve the generalization performance. However, the two goals of ensemble pruning, i.e., maximizing the generalization performance and minimizing the number of base learners...

The optimization of algorithm performance by automatically identifying good parameter settings is an important problem that has recently attracted much attention in the discrete optimization community. One promising approach constructs predictive performance models and uses them to focus attention on promising regions of a design space. Such methods have become quite sophisticated and have achi...

olive oil is one of the oldest known vegetable oils, ranking sixth in world production. since it is extractedfrom fresh fruit and used without aging, the conditions of oil production are very important. during theextraction of virgin olive oil, the milling and malaxation steps are vital for increasing oil yield and quality.in this study, the effect of milling (single or double), temperature of ...