融合多向学习的混沌麻雀搜索算法

对于原始麻雀搜索算法（SSA）在迭代过程中表现出的种群多样性减小，易陷入局部最优等问题，提出一种融合多向学习的混沌麻雀搜索算法（MSSA）。利用Hénon混沌映射初始化种群，增加麻雀种群的多样性，扩大可行解的搜索范围，为全局寻优奠定基础；采用多向学习策略增加麻雀跟随者探索未知领域的机会，平衡算法的局部开发性能和全局搜索能力；当算法陷入局部最优时，引用遗传算法中的变异策略依据动态的变异概率对当前最优个体进行扰动变异；将MSSA算法应用到无线传感器网络节点覆盖优化问题。数值实验结果与Wilcoxon秩和检验结果均表明MSSA算法在收敛精度与收敛速度等方面具有更明显的优势。

Chaotic Sparrow Search Algorithm Based on Multi-Directional Learning

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.