Review and empirical analysis of sparrow search algorithm

In recent years, swarm intelligence algorithms have received extensive attention and research. Swarm intelligence algorithms are a biological heuristic method, which is widely used in solving optimization problems. The traditional swarm intelligence algorithms provide new ideas and new ways to solve some practical problems, and they have made positive progress in fields such as combinatorial optimization, task scheduling, process control, engineering prediction, and image processing. In particular, the sparrow search algorithm is a new type of group intelligence optimization algorithm inspired by the group foraging behavior to perform local and global search by imitating the foraging and anti-predation behavior of sparrows. In view of the shortcomings of the original sparrow search algorithm, such as its easy fall into local optimum, slow convergence speed, and low convergence accuracy, scholars at home and abroad have improved the sparrow search algorithm and have made practical applications in various fields. Firstly, this paper introduces the basic principle of sparrow search algorithm, analyzes the factors affecting the performance of the algorithm, further proposes the improvement strategy of the algorithm, and performs function test comparison and performance analysis with particle swarm optimization algorithm, monarch butterfly algorithm, colony spider algorithm, and pigeon swarm optimization algorithm. After that, the application and development of the sparrow search algorithm in power grid load forecasting, image processing, path tracking, wireless sensor network routing performance optimization, wireless location, and fault diagnosis are described. Finally, combined with the performance characteristics and application direction of the sparrow search algorithm, the future research and development direction of the sparrow search algorithm is prospected.