关键词: 麻雀算法, t分布反向学习, 黏菌算法, 混沌映射, 图像分割

Abstract: In order to solve the problem that the sparrow search algorithm is easy to fall into local optimum and lack of search ability in the process of optimization, a chaos improved sparrow search algorithm（SMSSA） is proposed, which combines the improved slime mold algorithm. Firstly, the Bernoulli chaotic map is used to improve the initial population quality of the algorithm; then the following optimization strategy of the spiral slime mold algorithm is combined in the follower position update to improve the global search ability of the sparrow algorithm in the iterative process; finally, the t-distribution opposition-based-learning strategy is used to perturb the position of the sparrow search algorithm to improve the algorithm’s ability to jump out of the local optimum. In the simulation experiment, the algorithm is compared with other four basic algorithms based on 13 benchmark functions. The results show that the algorithm has good convergence and accuracy, and the global exploration ability is greatly improved compared with the original algorithm. Finally, SMSSA is applied to the Kapur entropy multi-threshold image segmentation task, and the results show that SMSSA has higher segmentation accuracy than the other four basic algorithms.

Key words: sparrow search algorithm, t-distribution opposition-based-learning, slime mould algorithm, chaotic mapping, image segmentation