Improved Genetic Algorithm With Application To Beam-forming For Smart Antenna

Genetic algorithm (GA) is a random search method based on the biological evolution. It works on the natural selection mechanism of the survival of the fittest. It is simple, robust and is fit to be used in the parallel computation. Although it holds very excellent global search performance, GA has some shortcomings such as slow convergence speed, easy to be premature, bad local search capability. And it can't meet the requirements of search speed and solution precision simultaneously. In order to solve complex, multi-variable optimal problems, based on the systemic analysis, a great deal of research works have been done in this thesis:First, a new combined algorithm is proposed based on the GA and direct search method. This combined algorithm has both the capability of optimization of GA in the global area and that of direct search method in the local area. Simulations have been carried out on the PID controller tuning and the results show the combined algorithm greatly improved the search speed and consistence of the search results is better than that of the genetic annealing strategy.Second, in order to increase the speed of solving traveling salesman problem (TSP) by GA, an improved adaptive GA with regional searching is proposed. The whole search space is divided into regional spaces. The better local gene sequences obtained by the regional searching are used for the global search. Simulations have been carried out on TSP and the results show the improved algorithm is robust, efficient, and can accelerate convergence speed.Third, based on the systemic analysis of GA, a local mutation operator is presented. The improved algorithm enhances its convergence speed and the precision.At last, the combined genetic algorithm proposed in this thesis is applied to the Beam-forming process for smart antenna. Simulation on CDMA system shows that the algorithm can form effective beams only in a few evolutional epochs.