关键词: 麻雀搜索算法, 差分变异, 混沌映射, 反向学习

Abstract: In view of the shortcomings of the sparrow search algorithm（SSA） in solving complex optimization problems, such as slow convergence speed, severe population convergence and being easy to fall into local optimum, a multi-strategy improved sparrow search algorithm（MISSA） is proposed. First, the chaotic mapping and reverse learning mechanisms are applied to improve the quality of the initial population. Then, the learning strategy of particle swarm algorithm is introduced to improve the information communication ability of the population and balance the performances of global exploration and local development of the algorithm. Finally, the mutation and cross operations of the differential evolution algorithm are used to enhance the escape power from the local optimal value. Experiments with eight benchmark functions show that the proposed algorithm has better optimization performance and convergence efficiency. Furthermore, the proposed algorithm is applied to optimize the parameters of support vector regression（SVR） model and its effectiveness is demonstrated with five selected UCI datasets.

Key words: sparrow search algorithm, differential mutation, chaotic mapping, opposition-based learning