Optimization of Ideal Reflector for Radio Telescope Based on Improved Composite Algorithm

The traditional quantum genetic algorithm lacks convergence and is easy to enter local optimal problems, so we improve the steps of population initialization, including niche technology, chaos mutation, etc. These measures can solve the problems such as too fast convergence speed to some extent. In this paper, the algorithms that go through these operations are denoted as improved composite algorithms. It can be found that the improved composite algorithm basically reaches the optimal value when the 21st generation is around, however, the quantum genetic algorithm can reach the optimum only when the algorithm operation reaches about 35 generations. In addition, the improved composite algorithm has basically reached the optimal value of −0.9356 when iterated to about the 21st generation, while the traditional quantum genetic algorithm is still at a low level. As a result, the improved composite algorithms has quicker convergence speed and better searching ability than the traditional quantum genetic algorithm. Therefore, the improved composite algorithm is better than the traditional quantum genetic algorithm in solving the ideal reflector optimization problem based on FAST.