Research on Uncertainty Intelligent Planning Algorithm

There is an objective or artificial uncertain optimization problem in many area, the traditional methods are difficult to solve such problems. The paper firstly introduces the principle and structure of the traditional quantum genetic algorithm (QGA), analyze the main problem of the traditional quantum genetic algorithm, namely the problem of the solution space conversion, and how to determine the rotational phase of the quantum gate. Then the paper improves the algorithm based on the analysis, gives the process of improved quantum genetic algorithm (IQGA), and takes Shaffer´s F1 multimodal uncertain planning for example, analyze the properties of the running efficiency and the convergence efficiency etc. Of IQGA. The simulation results show that: the running efficiency of IQGA is higher, and convergence efficiency is faster, therefore, the uncertain planning problem can be better support by IQGA.