To enhance the coverage rate of Wireless Sensor Networks (WSNs), this paper proposes an advanced optimization strategy based on a multi-strategy integrated Northern Goshawk Optimization (NGO) algorithm. Specifically, multivariate chaotic mapping is first employed to improve the randomness and uniformity of the initial population. To further bolster population diversity and prevent the algorithm from stagnating in local optima, a bidirectional population evolutionary dynamics strategy is incorporated following the pursuit-and-evasion phase, thereby facilitating the attainment of the global optimal solution. Extensive simulations were conducted to evaluate the performance of the proposed multi-strategy NGO in WSN coverage. Experimental results demonstrate that the proposed algorithm significantly outperforms existing benchmarks in terms of both coverage enhancement and node connectivity.

翻译：为提高无线传感器网络（WSN）的覆盖率，本文提出了一种基于多策略融合北方苍鹰优化（NGO）算法的先进优化策略。具体而言，首先采用多元混沌映射以提升初始种群的随机性与均匀性。为进一步增强种群多样性并防止算法陷入局部最优，在追击与逃逸阶段后引入了双向种群演化动力学策略，从而促进全局最优解的获取。通过大量仿真实验评估了所提多策略NGO算法在WSN覆盖中的性能。实验结果表明，所提算法在覆盖增强与节点连通性方面均显著优于现有基准算法。