Application of improved sparrow search algorithm in SVM optimization
Chengtian Ouyang, Donglin Zhu, Fengqi Wang · Journal of Physics Conference Series · 2021
Abstract Sparrow search algorithm has good global search performance, but there is still a probability of falling into local optimum. In order to optimize the algorithm, K-means is proposed to make the population evenly distributed and improve the efficiency at the beginning. Then, the sine-cosine search and adaptive local search strategies are introduced to reduce the probability of falling into the local optimum in the middle and late stages, so that the optimal solution can be obtained easily. Finally, the two strategies are discussed and applied to SVM parameter optimization. The UCI dataset classification results show that the algorithm with the two strategies is suitable for SVM parameter optimization, and the algorithm with sine-cosine search has better optimization ability and stability.