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

摘要： 为解决麻雀搜索算法（sparrow search algorithm）在寻优过程中容易陷入局部最优、搜索能力不足的问题，提出了一种融合黏菌算法改进的混沌改进麻雀搜索算法（SMSSA）。利用Bernoulli混沌映射提高算法初始种群质量；在跟随者位置更新中结合螺旋黏菌搜索策略的跟随寻优策略以提高麻雀算法在迭代过程中的全局搜索能力；利用t分布反向学习策略对麻雀位置进行扰动，以提高算法跳出局部最优的能力。在仿真实验中将该算法与其他四种基本算法基于13种基准测试函数进行对比实验，结果表明该算法具有良好的收敛性以及精度，且全局探索能力相较于原算法大大提高。将SMSSA应用于Kapur熵多阈值图像分割任务中，结果表明SMSSA相较于其他四种基本算法有着更高的分割精度。

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