改进布谷鸟搜索算法及在无线传感器网络中的应用  

Improved Cuckoo Search Algorithm and Its Application in WSN

作　　者：程晶晶[1] CHENG Jingjing(Anhui Technical College of Mechanical and Electrical Engineering,241000,Wuhu,Anhui,China)

机构地区：[1]安徽机电职业技术学院,安徽芜湖241000

出　　处：《淮北师范大学学报（自然科学版）》2024年第3期66-72,共7页Journal of Huaibei Normal University：Natural Sciences

基　　金：安徽省高校自然科学研究重点项目(2023AH052698);芜湖市重点研发与成果转化项目(2023yf124);安徽省中青年教师培养行动学科(专业)带头人培育项目(DTR2023101)。

摘　　要：针对布谷鸟搜索算法存在收敛速度慢和易陷入局部最优等缺陷,提出一种改进布谷鸟搜索算法。改进算法采用轮盘赌选择鸟巢并精英引导Levy飞行用于鸟巢位置更新,以加快算法收敛速度;同时引入生物地理算法的迁入率和迁出率模型,以不同概率执行黄金正弦算法;最后对鸟巢实行差分进化算法改善种群的多样性,从而避免算法陷入局部最优并提高全局搜索能力。通过15个基准函数和3个场景的WSN仿真实验并与相关文献比较,结果表明改进算法行之有效。An improved cuckoo search algorithm is proposed to address the defects of slow convergence speed and easy trapping into local optima in the original cuckoo search algorithm.The improved algorithm uses roulette wheel to select the bird′s nest and elite guide Levy to fly for updating the bird′s nest position in order to accelerate the convergence speed of the algorithm.Additionally,the migration rate and emigration rate models from the biogeography-based algorithm are introduced to execute the Golden Sine Algorithm with different probabilities.Finally,the nest positions are modified using the differential evolution algorithm to improve the diversity of the population,thus avoiding the algorithm from getting trapped into local optima and enhancing the global search capability.Fifteen benchmark functions and simulation experiments of wireless sensor networks in three different scenarios are conducted to evaluate the effectiveness of the proposed algorithm in comparison with related literature,and the results demonstrate the effectiveness of the proposed algorithm.

关 键 词：布谷鸟搜索算法 差分进化算法 基准函数 无线传感器网络 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]