Learning Sparrow Algorithm With Non-Uniform Search for Global Optimization

Abstract

Sparrow Algorithm as a New Swarm Intelligence Search Algorithm, the sparrow algorithm has good optimization ability, but in complex environments, it still has certain limitations, such as weak learning ability. Therefore, this paper proposes a learning sparrow search algorithm for non-uniform search(Sparrow search algorithm with non-uniform search, NSSSA). A learning behavior selection strategy is proposed, and saltation learning and a random walk learning are introduced respectively.To a certain extent, the algorithm avoided alling into the local optimum, and a non-uniform variable spiral search is proposed to balance the development and search capabilities of the algorithm. In the experimental simulation, the effectiveness of the NSSSA algorithm is verified by using the benchmark function, and it is tested on the CEC 2013 test set. Compared with the algorithms with better performance in recent years, the results show that the NSSSA algorithm has better universality . Finally, the NSSSA algorithm is applied to the WSN coverage optimization problem. The results show that NSSSA achieves more than 90% and 96% coverage on the two models of 50×50 and 100×100, respectively, which verifies the practicability of the algorithm.

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

“…Tang et al [30] addressed the challenge of optimizing hyperparameters in neural networks by introducing the Particle Swarm Optimization (PSO) evolutionary algorithm into the efficient network. Chen et al [31] addressed the weak learning ability of the Sparrow Search Algorithm (SSA) in complex environments by proposing a Non-Uniform Search Sparrow Search Algorithm (NSSSA). The NSSSA exhibited strong optimization and learning capabilities.…”

“…This study is based on the Fast Fourier Transform (FT) algorithm to generate significant regions of the end-face of metal gears. The algorithm first performs Gaussian filtering on the original image and converts it to the Lab color space [31]. Then, the Euclidean distances between the three channels (L, a, b) and the mean images (lm, am, bm) of the corresponding channels in the Lab color space are computed separately to obtain the saliency value (sm), as shown in Equation ( 1).…”