Wang Guohao, Li Xun
School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China.
Sensors (Basel). 2024 Dec 26;25(1):69. doi: 10.3390/s25010069.
To solve the coverage problem caused by the random deployment of wireless sensor network nodes in the forest fire-monitoring system, a modified marine predator algorithm (MMPA) is proposed. Four modifications have been made based on the standard marine predator algorithm (MPA). Firstly, tent mapping is integrated into the initialization step to improve the searching ability of the early stage. Secondly, a hybrid search strategy is used to enhance the ability to search and jump out of local optimum. Thirdly, the golden sine guiding mechanism is applied to accelerate the convergence of the algorithm. Finally, a stage-adjustment strategy is proposed to make the transition of stages more smoothly. Six specific test functions chosen from the CEC2017 function and the benchmark function are used to evaluate the performance of MMPA. It shows that this modified algorithm has good optimization capability and stability compared to MPA, grey wolf optimizer, sine cosine algorithm, and sea horse optimizer. The results of coverage tests show that MMPA has a better uniformity of node distribution compared to MPA. The average coverage rates of MMPA are the highest compared to the commonly used metaheuristic-based algorithms, which are 91.8% in scenario 1, 95.98% in scenario 2, and 93.88% in scenario 3, respectively. This demonstrates the superiority of this proposed algorithm in coverage optimization of the wireless sensor network.
为解决森林火灾监测系统中无线传感器网络节点随机部署所导致的覆盖问题，提出了一种改进的海洋捕食者算法（MMPA）。该算法在标准海洋捕食者算法（MPA）的基础上进行了四处改进。首先，将帐篷映射融入初始化步骤，以提高算法早期的搜索能力。其次，采用混合搜索策略，增强搜索能力并跳出局部最优。第三，应用黄金正弦引导机制，加速算法收敛。最后，提出阶段调整策略，使各阶段的过渡更加平滑。选用CEC2017函数和基准函数中的六个特定测试函数来评估MMPA的性能。结果表明，与MPA、灰狼优化器、正弦余弦算法和海马优化器相比，该改进算法具有良好的优化能力和稳定性。覆盖测试结果表明，与MPA相比，MMPA具有更好的节点分布均匀性。与常用的基于元启发式的算法相比，MMPA的平均覆盖率最高，在场景1中为91.8%，在场景2中为95.98%，在场景3中为93.88%。这证明了该算法在无线传感器网络覆盖优化方面的优越性。
