关键词: 麻雀搜索算法, Hénon混沌映射, 多向学习策略, 变异算子, 无线传感器网络

Abstract: Sparrow search algorithm in the iterative process has poor performance such as the low population diversity and the tendency of falling into local optima. To address the mentioned problems, a chaotic sparrow search algorithm based on multi-directional learning is proposed in this paper. The Hénon chaotic sequence is employed to initialize the population, the proposed initialization method increases the diversity of the sparrow population, and expands the search range of feasible solutions, which can lay the foundation for global optimization. The multi-directional learning strategy is adopted to increase the opportunity of sparrow followers to explore unknown areas, and to balance the local development performance and global search ability of the algorithm. Moreover, the mutation strategy of genetic algorithm is used to disturb and mutate the current optimal individual according to the dynamic mutation probability while the algorithm trapped in the local optimum. The MSSA algorithm is applied to the wireless sensor network node coverage optimization problem. The numerical experiment results and the Wilcoxon rank sum test results show that the MSSA algorithm has more obvious advantages in terms of convergence accuracy and convergence speed.

Key words: sparrow search algorithms, Hénon chaos mapping, multi-directional learning strategy, variational operators, wireless sensor network