Improved Genetic Algorithms Based on Chaotic Mutation Operation and Its Application

Abstract

Traditional genetic algorithm is advanced methods in solving complex nonlinear optimization problems at present, but exists its own defection such as local convergence. To solve the issue, considering chaotic algorithm's randomness, ergodicity, regularity and strong sensitivity of changes to the initial value which base on the robot path planning problem. From this perspective, the paper conducts a kind of chaos genetic algorithm for intelligent integration, it gives a detailed in-depth analysis and research thoroughly about genetic algorithm and the combination of chaos optimization algorithm. it uses the chaotic variables on the current point disturbance, with a gradual decrease in-depth search range of disturbance, to solve local convergence of single genetic algorithms. At last, the algorithm is applied to the specific issue of robot path planning simulation. The result shows that the method can significantly improve the solving global optimization problems of computational efficiency.