多策略协同改进的造纸厂WSN部署布谷鸟算法  

Multi-Strategy Cooperative Improvement of Cuckoo Search Algorithm for WSN Deployment in Paper Mills

作　　者：曹京年 张育洋 李珑[1] CAO Jingnian;ZHANG Yuyang;LI Long(School of Aeronautical Engineering,Shaanxi Polytechnic Institute,Xianyang 712000,China)

机构地区：[1]陕西工业职业技术学院航空工程学院,陕西咸阳712000

出　　处：《造纸科学与技术》2025年第3期88-92,113,共6页Paper Science And Technology

基　　金：陕西工业职业技术学院科研基金资助项目(2023YKYB-006)。

摘　　要：针对标准布谷鸟算法求解无线传感网节点部署问题时存在节点位置聚集、覆盖率较低等不足,提出一种多策略协同改进的布谷鸟算法。首先设计兼顾探索与开发的双种群寻优策略平衡算法全局探索与局部开发;其次引入融合随机变量的开发步长调整机制,提高算法局部搜索质量,再次对个体施加差异化发现概率,降低随机扰动对优质个体的影响,提高算法工作效率;最后引入基于高斯扰动的种群变异策略,增强算法跳出局部最优解的能力。以无线传感网覆盖率最大为目标函数设计对比实验。实验结果表明,改进算法具备较强的求解能力,能够有效优化节点部署位置,提高节点覆盖率。To address the issues of node position aggregation and low coverage rate when solving the wireless sensor network(WSN)node deployment problem using the standard Cuckoo Search algorithm,a multi-strategy cooperative improved Cuckoo Search algorithm is proposed.First,a dual-population optimization strategy that balances global exploration and local exploitation is designed to enhance the algorithm's overall performance.Second,a development step adjustment mechanism integrating random variables is introduced to improve the quality of the algorithm's local search.Third,differential discovery probabilities are applied to individual solutions to reduce the impact of random disturbances on high-quality solutions,thereby improving the algorithm's efficiency.Finally,a population mutation strategy based on Gaussian perturbation is incorporated to enhance the algorithm's ability to escape local optima.Comparative experiments,with the WSN coverage rate maximization as the objective function,demonstrate that the improved algorithm possesses strong problem-solving capabilities,effectively optimizes node deployment positions,and increases node coverage rate.

关 键 词：无线传感网 布谷鸟算法 发现概率 种群变异 覆盖率 

分 类 号：TS73[轻工技术与工程—制浆造纸工程]