Based On Chaos Theory, Genetic Algorithm Improved And Applied Research

Genetic algorithm is a new global optimization algorithm, and a lot of inspiring achievements have been made in many fields. But in engineering practice, premature convergence often happens, and sometimes the speed of convergence is very slow, which greatly restrict the application of Genetic algorithm.Firstly, the basic concepts of genetic algorithm and chaos theory are introduced in this thesis. After analyzing the adaptive genetic algorithm presented by M.Srinivas, a modified adaptive strategy has been proposed. Secondly, genetic algorithms based on chaotic switch and chaos optimization are studied respectively, which solve the function optimization problem successfully. Next, the multicast routing problem based on genetic algorithm is researched in this paper. A new method that searches effective gene with chaotic variable is presented and it can find the optimal multicast routing quickly. Finally, the improved genetic algorithm has solved the multicast routing problem with QoS constrained successfully and satisfactory results have been made.Simulation results show that these modified methods can effectively alleviate the problem of premature convergence and greatly increase the speed of global convergence. The exploration of this paper will be beneficial for the spread of genetic algorithm.