Genetic Algorithm for Optimization: Preprocessing with n Dimensional Bisection and Error Estimation

A knowledge of the appropriate values of the parameters of a genetic algorithm (GA) such as the population size, the shrunk search space containing the solution, crossover and mutation probabilities is not available a priori for a general optimization problem. Recommended here is a polynomial-time preprocessing scheme that includes an n-dimensional bisection and that determines the foregoing parameters before deciding upon an appropriate GA for all problems of similar nature and type. Such a preprocessing is not only fast but also enables us to get the global optimal solution and its reasonably narrow error bounds with a high degree of confidence

Source localization within a uniform circular sensor array

Traditional source localization problems have been considered with linear and planar antenna arrays. In this research work, we assume that the sources are located within a uniformly spaced circular sensor array. Using a modified Metropolis algorithm and Polak-Ribière conjugate gradients, a hybrid optimization algorithm is proposed to localize sources within a two dimensional uniform circular sensor array, which suffers from far field attenuation. The developed algorithm is capable of accurately locating the position of a single, stationary source within 1% of a wavelength and 1° of angular displacement. In the single stationary source case, the simulated Cramer-Rao Lower Bound has also shown low noise susceptibility for a reasonable signal to noise ratio. Additionally, the localization of multiple stationary sources within the array is presented and tracking capabilities for a slowly moving non-stationary source is also demonstrated. In each case, results are presented, analyzed and discussed. Furthermore, the proposed algorithm has also been validated through hardware experimentation. The design and construction of four microstrip patch antennas and a wire antenna have been completed to emulate a circular sensor array and the enclosed source, respectively. Within this array, data has been collected at the four sensors from several fixed source positions and fitted into the proposed algorithm for source localization. The convergence of the algorithm with both simulated data and data collected from hardware are compared and sources of error and potential improvements are proposed

Optimization of Heterogeneous UAV Communications Using the Multiobjective Quadratic Assignment Problem

The Air Force has placed a high priority on developing new and innovative ways to use Unmanned Aerial Vehicles (UAVs). The Defense Advanced Research Projects Agency (DARPA) currently funds many projects that deal with the advancement of UAV research. The ultimate goal of the Air Force is to use UAVs in operations that are highly dangerous to pilots, mainly the suppression of enemy air defenses (SEAD). With this goal in mind, formation structuring of autonomous or semi-autonomous UAVs is of future importance. This particular research investigates the optimization of heterogeneous UAV multi-channel communications in formation. The problem maps to the multiobjective Quadratic Assignment Problem (mQAP). Optimization of this problem is done through the use of a Multiobjective Evolutionary Algorithm (MOEA) called the Multiobjective Messy Genetic Algorithm - II (MOMGA-II). Experimentation validates the attainment of an acceptable Pareto Front for a variety of mQAP benchmarks. It was observed that building block size can affect the location vectors along the current Pareto Front. The competitive templates used during testing perform best when they are randomized before each building block size evaluation. This tuning of the MOMGA-II parameters creates a more effective algorithm for the variety of mQAP benchmarks, when compared to the initial experiments. Thus this algorithmic approach would be useful for Air Force decision makers in determining the placement of UAVs in formations