Multi Strategy Improved Sparrow Search Algorithm Based on Rough Data Reasoning

Aiming at the problem that the diversity of sparrow search algorithm is reduced and it is easy to fall into local optimum in the iterative process, a multi strategy improved sparrow search algorithm (RSSA) based on rough data-deduction is proposed. Firstly, the algorithm initializes the population with the idea of low difference sequence to enhance the global search ability of the algorithm and ensure the integrity of rough data reasoning domain. Then, the rough reasoning data theory is introduced, and the relationship between individuals is established by combining fitness and distance, so as to improve the convergence speed and the ability to jump out of the local optimum. Moreover, the over bounded individuals in the iteration are assigned to the value near the boundary instead of the maximum or minimum value of the boundary at the same time, which ensures the diversity of the population and improves the convergence speed of the algorithm. Compared with the other three algorithms and traditional sparrow search algorithm, the simulation results based on 11 test functions show that RSSA has faster convergence speed, higher accuracy and better effect in the face of multi peak problems. © 2022, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.