In this paper, we propose a new metaheuristic algorithm based on Lévy flight called Lévy flight distribution (LFD) for solving real optimization problems. The LFD algorithm is inspired from the Lévy flight random walk for exploring unknown large search spaces (e.g., wireless sensor networks (WSNs). To assess the performance of the LFD algorithm, various optimization test bed problems are considered, namely the congress on evolutionary computation (CEC) 2017 suite and three engineering optimization problems: tension/compression spring, the welded beam, and pressure vessel. The statistical simulation results revealed that the LFD algorithm provides better results with superior performance in most tests compared to several well-known metaheuristic algorithms such as simulated annealing (SA), differential evolution (DE), particle swarm optimization (PSO), elephant herding optimization (EHO), the genetic algorithm (GA), moth-flame optimization algorithm (MFO), whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA), and Harris Hawks Optimization (HHO) algorithm. Furthermore, the performance of the LFD algorithm is tested on other different optimization problems of unknown large search spaces such as the area coverage problem in WSNs. The LFD algorithm shows high performance in providing a good deployment schema than energy-efficient connected dominating set (EECDS), A3, and CDS-Rule $K$ topology construction algorithms for solving the area coverage problem in WSNs. Eventually, the LFD algorithm performs successfully achieving a high coverage rate up to 43.16 %, while the A3, EECDS, and CDS-Rule $K$ algorithms achieve low coverage rates up to 40 % based on network sizes used in the simulation experiments. Also, the LFD algorithm succeeded in providing a better deployment schema than A3, EECDS, and CDS-Rule $K$ algorithms and enhancing the detection capability of WSNs by minimizing the overlap between sensor nodes and maximizing the coverage rate. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/76103-lfd.

在本文中，我们提出了一种基于Lévy飞行的新元启发式算法，称为Lévy飞行分布（LFD），用于解决实际的优化问题。LFD算法是从Lévy飞行随机行走中启发而来的，用于探索未知的大型搜索空间（例如无线传感器网络（WSN））。为了评估LFD算法的性能，考虑了各种优化测试平台问题，即进化计算大会（CEC）2017套件和三个工程优化问题：拉伸/压缩弹簧，焊接梁和压力容器。统计仿真结果表明，与几种著名的元启发式算法相比，LFD算法在大多数测试中提供更好的结果，并且性能优异例如模拟退火（SA），差分演化（DE），粒子群优化（PSO），象群优化（EHO），遗传算法（GA），蛾-火焰优化算法（MFO），鲸鱼优化算法（WOA），蚱hopper优化算法（GOA）和哈里斯霍克斯优化（HHO） ）算法。此外，在未知大搜索空间的其他不同优化问题（例如WSN中的区域覆盖问题）上测试了LFD算法的性能。LFD算法在提供良好的部署方案方面表现出比节能连接控制集（EECDS），A3和CDS-Rule更高的性能。在未知大搜索空间的其他不同优化问题（例如WSN中的区域覆盖问题）上测试了LFD算法的性能。LFD算法在提供良好的部署方案方面表现出比节能连接控制集（EECDS），A3和CDS-Rule更高的性能。在未知大搜索空间的其他不同优化问题（例如WSN中的区域覆盖问题）上测试了LFD算法的性能。LFD算法在提供良好的部署方案方面表现出比节能连接控制集（EECDS），A3和CDS-Rule更高的性能。$ķ$用于解决无线传感器网络中的区域覆盖问题的拓扑构造算法。最终，LFD算法成功完成了高达43.16％的高覆盖率，而A3，EEDCS和CDS-Rule$ķ$基于模拟实验中使用的网络大小，该算法可实现高达40％的低覆盖率。此外，LFD算法成功地提供了比A3，EEDCS和CDS-Rule更好的部署方案$ķ$通过最大程度地减少传感器节点之间的重叠并最大化覆盖率来提高WSN的算法性能。该源代码当前可从以下网址公开获得：https：//www.mathworks.com/matlabcentral/fileexchange/76103-lfd。