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<p>This manuscript proposes an Improved Sparrow Search Algorithm with Multi-Strategies (MSISSA) to address the shortcomings of the Sparrow Search Algorithm (SSA), and applies it to optimize the deployment of 3D Wireless Sensor Nodes (3D-WSNs). The improvements of MSISSA encompass three main components: firstly, it utilizes an adaptive weight operator for updating position to enhance local search accuracy and expedite convergence; secondly, it introduces the spiral flight mechanism of the Moth Flame Optimization (MFO) algorithm, which balances the algorithm’s local and global search abilities; finally, it uses the Levy flight mechanism to jump out the local optima. The experimental results demonstrate that MSISSA outperforms SSA, its four variants, and two classical intelligent algorithms. The simulation results indicate that when the number of nodes is 30 and 50, the optimal coverage rate of MSISSA reaches 91.89% and 99.11%, respectively, surpassing those achieved by SSA and its four variants, as well as two classical intelligent algorithms. Therefore, it can be concluded that MSISSA is well-suited for optimizing the deployment of 3D-WSNs.</p>

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An improved sparrow search algorithm and its application in the deployment of 3D wireless sensor nodes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月15日

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  • Shan Li,
  • Li Ping Mo,
  • Biao Yin,
  • Wei Min

摘要

This manuscript proposes an Improved Sparrow Search Algorithm with Multi-Strategies (MSISSA) to address the shortcomings of the Sparrow Search Algorithm (SSA), and applies it to optimize the deployment of 3D Wireless Sensor Nodes (3D-WSNs). The improvements of MSISSA encompass three main components: firstly, it utilizes an adaptive weight operator for updating position to enhance local search accuracy and expedite convergence; secondly, it introduces the spiral flight mechanism of the Moth Flame Optimization (MFO) algorithm, which balances the algorithm’s local and global search abilities; finally, it uses the Levy flight mechanism to jump out the local optima. The experimental results demonstrate that MSISSA outperforms SSA, its four variants, and two classical intelligent algorithms. The simulation results indicate that when the number of nodes is 30 and 50, the optimal coverage rate of MSISSA reaches 91.89% and 99.11%, respectively, surpassing those achieved by SSA and its four variants, as well as two classical intelligent algorithms. Therefore, it can be concluded that MSISSA is well-suited for optimizing the deployment of 3D-WSNs.