Electrical Engineering College, Guizhou University, Guiyang 550025, China.
Power China Guizhou Electric Power Engineering Co., Ltd., Guiyang 550025, China.
Sensors (Basel). 2022 Apr 28;22(9):3383. doi: 10.3390/s22093383.
To address the problems of uneven distribution and low coverage of wireless sensor network (WSN) nodes in random deployment, a node coverage optimization strategy with an improved COOT bird algorithm (COOTCLCO) is proposed. Firstly, the chaotic tent map is used to initialize the population, increase the diversity of the population, and lay the foundation for the global search for the optimal solutions. Secondly, the Lévy flight strategy is used to perturb the individual positions to improve the search range of the population. Thirdly, Cauchy mutation and an opposition-based learning strategy are fused to perturb the optimal solutions to generate new solutions and enhance the ability of the algorithm to jump out of the local optimum. Finally, the COOTCLCO algorithm is applied to WSN coverage optimization problems. Simulation results show that COOTCLCO has a faster convergence speed and better search accuracy than several other typical algorithms on 23 benchmark test functions; meanwhile, the coverage rate of the COOTCLCO algorithm is increased by 9.654%, 13.888%, 6.188%, 5.39%, 1.31%, and 2.012% compared to particle swarm optimization (PSO), butterfly optimization algorithm (BOA), seagull optimization algorithm (SOA), whale optimization algorithm (WOA), Harris hawks optimization (HHO), and bald eagle search (BES), respectively. This means that in terms of coverage optimization effect, COOTCLCO can obtain a higher coverage rate compared to these algorithms. The experimental results demonstrate that COOTCLCO can effectively improve the coverage rate of sensor nodes and improve the distribution of nodes in WSN coverage optimization problems.
为了解决无线传感器网络（WSN）节点随机部署中存在的分布不均匀和覆盖范围低的问题，提出了一种基于改进的 COOT 鸟算法（COOTCLCO）的节点覆盖优化策略。首先，使用混沌帐篷映射来初始化种群，增加种群的多样性，为寻找最优解的全局搜索奠定基础。其次，使用 Lévy 飞行策略来扰动个体位置，提高种群的搜索范围。第三，融合 Cauchy 变异和基于对立的学习策略来扰动最优解，生成新的解，增强算法跳出局部最优的能力。最后，将 COOTCLCO 算法应用于 WSN 覆盖优化问题。仿真结果表明，COOTCLCO 在 23 个基准测试函数上的收敛速度比其他几种典型算法更快，搜索精度更高；同时，COOTCLCO 算法的覆盖率比粒子群优化（PSO）、蝴蝶优化算法（BOA）、海鸥优化算法（SOA）、鲸鱼优化算法（WOA）、哈里斯鹰优化算法（HHO）和白头鹰搜索算法（BES）分别提高了 9.654%、13.888%、6.188%、5.39%、1.31%和 2.012%。这意味着在覆盖优化效果方面，COOTCLCO 可以比这些算法获得更高的覆盖率。实验结果表明，COOTCLCO 可以有效提高传感器节点的覆盖率，改善 WSN 覆盖优化问题中节点的分布。
