[1]周涛,柯鹏.多策略增强的麻雀搜索算法及其应用[J].计算机技术与发展,2025,(06):100-107.[doi:10.20165/j.cnki.ISSN1673-629X.2025.0028]
　ZHOU Tao,KE Peng.Multi-strategy Enhanced Sparrow Search Algorithm and Its Application[J].,2025,(06):100-107.[doi:10.20165/j.cnki.ISSN1673-629X.2025.0028]
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多策略增强的麻雀搜索算法及其应用()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年06期
页码:
100-107
栏目:
人工智能
出版日期:
2025-06-10

文章信息/Info

Title:
Multi-strategy Enhanced Sparrow Search Algorithm and Its Application
文章编号:
1673-629X(2025)06-0100-08
作者:
周涛1; 2; 柯鹏1; 2
1. 武汉科技大学 计算机科学与技术学院,湖北 武汉 430065;
2. 智能信息处理与实时工业系统湖北省重点实验室,湖北 武汉 430065
Author(s):
ZHOU Tao1; 2; KE Peng1; 2
1. School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China;
2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System,Wuhan 430065,China
关键词:
麻雀搜索算法; Tent映射; 黄金正弦; t分布扰动; 精英差分变异; 无线传感器网络
Keywords:
sparrow search algorithm; Tent mapping;  golden sine;  t - distribution disturbance;  elite differential mutation perturbation;  wireless sensor network
分类号:
TP301.6
DOI:
10.20165/j.cnki.ISSN1673-629X.2025.0028
摘要:
为应对麻雀搜索算法初始种群多样性匮乏、位置更新方式单调且易于陷入局部最优等问题,提出一种多策略增强的麻雀搜索算法。 首先采用改进后的 Tent 映射来丰富算法的初始种群,以拓宽搜索范围。 同时,在发现者中引入自适应权重的黄金正弦策略,实现局部与全局搜索的平衡。 其次,引入惯性权重改进跟随者更新方式以提升算法的遍历性能。此外,针对不同适应度的个体分别采用 t 分布扰动和精英差分变异扰动,使算法能更有效地跳出局部最优。 最后,将改进算法在 12 个基准测试函数上同时与其他五种算法进行对比测试,随后将其应用于无线传感器网络的布局优化中。 结果表明,经过改进的算法在寻优精度和收敛速度方面均有显著提升,其优化后的无线传感器网络覆盖率高达 99. 18% ,明显高于其他算法。
Abstract:
For the Sparrow Search Algorithm’s ( SSA) challenges, including limited initial population diversity,monotonous position updating,and a propensity for becoming trapped in local optima,we propose a Multi - Strategy Enhanced Sparrow Search Algorithm (MSESSA). Initially,we enrich the algorithm’s initial population by utilizing an improved Tent mapping technique,thereby broadening the search scope. Additionally, we introduce an adaptive weighted golden sine strategy among the discoverers to achieve a balance between local and global searches. Furthermore,we incorporate inertial weights to refine the followers’ updating mechanism,enhancing the algorithm’s traversal capabilities. Moreover,we tailor perturbations using t-distribution and elite differential mutation to individuals of varying fitness levels, allowing the algorithm to escape local optima more effectively. Finally, we conduct comparative tests of the improved algorithm on 12 benchmark functions alongside five other algorithms,and subsequently apply it to the layout optimization of wireless sensor networks. The results reveal that the refined algorithm exhibits significant improvements in optimization accuracy and con-vergence speed,achieving an optimized wireless sensor network coverage rate of up to 99. 18% ,markedly surpassing other algorithms.

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更新日期/Last Update: 2025-06-10