摘要
无线传感器网络技术在随机部署过程中经常面临节点分布不均匀的挑战。为了提高节点部署覆盖率,提出了一种协同粒子群算法的乌鸦搜索算法。该方法将粒子群优化与乌鸦搜索算法相结合,使乌鸦搜索算法能够利用粒子群优化的全局最优位置信息,平衡全局和局部搜索能力。在初始化过程中,采用了Logistic混沌映射来处理种群的多样性。此外,引入自适应步长和Levy飞行,提高了算法逃避局部最优的能力,提高了收敛速度和优化精度。当应用于优化8个基准函数和部署WSN节点时,新算法始终优于其他智能算法,证明了其在函数优化和WSN节点部署方面的有效性。
Wireless Sensor Network(WSN)technology often faces the challenge of uneven node distribution during random deployment.To enhance node deployment coverage,we propose an optimization method that Synergetic Crow Search Algorithm for Particle Swarm Optimization(PCSA).In this approach,we integrate Particle Swarm Optimization(PSO)with the Crow Search Algorithm(CSA),allowing the crow search algorithm to utilize global optimal position information from PSO,balancing global and local search capabilities.We also employ logistic chaotic map for population diversity during initialization.Additionally,adaptive step size and Levy flight are introduced to improve the algorithm's ability to escape local optima,enhancing convergence speed and optimization accuracy.When applied to optimize 8 benchmark functions and deploy WSN nodes,PCSA consistently outperform other intelligent algorithms,demonstrating their effectiveness in function optimization and WSN node deployment.
作者
施达
曲良东
SHI Da;QU Liangdong(College of Artificial Intelligence,Guangxi Minzu University,Nanning 530006,China)
出处
《广西民族大学学报（自然科学版）》
CAS
2024年第2期87-98,共12页
Journal of Guangxi Minzu University ：Natural Science Edition
基金
广西科技基地与人才专项项目(桂科AD22080021)
广西高校中青年教师科研基础能力提升项目(2022KY0164)。
