This thesis explores genetic algorithm and rule-based optimization techniques used by cognitive radios to make operating parameter decisions. Cognitive radios take advantage of intelligent control methods by using sensed information to determine the optimal set of transmission parameters for a given situation. We have chosen to explore and compare two control methods. A biologically-inspired ge...

We investigate axioms that intuitively ought to be satisfied by graph clustering objective functions. Two tailored for graph clustering objectives are introduced, and the four axioms introduced in previous work on distance based clustering are reformulated and generalized for the graph setting. We show that modularity, a standard objective for graph clustering, does not satisfy all these axioms...

By using the penalty function method with objective parameters, the paper presents an interactive algorithm to solve the inequality constrained multi-objective programming (MP). The MP is transformed into a single objective optimal problem (SOOP) with inequality constrains; and it is proved that, under some conditions, an optimal solution to SOOP is a Pareto efficient solution to MP. Then, an i...

Clustering finds various applications in the field of medical and telecommunication for unsupervised learning which is much required in expert system and its application. Various algorithms have been developed to clustering for the past fifty years after the introduction of k-means clustering. Recently, optimization algorithms are applied for clustering to find optimal clusters with the help of...

We consider the problem of multi-objective maximization of monotone submodular functions subject to cardinality constraint, one formulation of which is max|A|=k mini∈{1,...,m} fi(A). Krause et al. (2008) showed that when the number of functions m grows as the cardinality k i.e., m = Ω(k), the problem is inapproximable (unless P = NP ). For the more general case of matroid constraint, Chekuri et...

Knowledge acquisition is one major bottle-neck in using planning systems. Model-lite planning reduces this burden by placing responsibility on the planning system to cope with partially specified models. For example, eliciting the planning objective can be difficult in applications where it is necessary to reason about multiple plan metrics, such as cost, time, risk, human life, etc. Traditiona...

Kinematic metric functions, which allow us to make metric measurements such as “distance”, “length” and “angle”, are often needed in robotics. However, some commonly used metric functions are not well-defined because of their dependence upon choice of reference frame. This paper presents formal and practical conditions for kinematic metric functions to be well-defined. In order to properly anal...

Distribution systems can be operated in multiple configurations since they are possible combinations of radial and loop feeders. Each configuration leads to its own power losses and reliability level of supplying electric energy to customers. In order to obtain the optimal configuration of power networks, their reconfiguration is formulated as a complex optimization problem with different objec...

Multi objective quadratic fractional programming (MOQFP) problem involves optimization of several objective functions in the form of a ratio of numerator and denominator functions which involve both contains linear and quadratic forms with the assumption that the set of feasible solutions is a convex polyhedral with a nite number of extreme points and the denominator part of each of the objecti...

We present a stochastic optimal control approach to wildlife management. The objective value is the present value of hunting and meat, reduced by the present value of the costs of plant damages and traffic accidents caused by the wildlife population. First, general optimal control functions and value functions are derived. Then, numerically specified optimal control functions and value func...