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基于BBO优化K-means算法的WSN分簇路由算法

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针对无线传感器网络中传感器节点能量有限、网络生存期短的问题,提出一种基于生物地理学算法优化K-means的无线传感器网络分簇路由算法BBOK-GA。成簇阶段,通过生物地理学优化算法改进K-means算法,避免求解时陷入局部最优。根据能量因子和距离因子设计了新的适应度函数选举最优簇首,完成分簇任务。数据传输阶段,则利用遗传算法为簇首节点搜寻到基站的最佳数据传输路径。仿真结果表明,相较于LEACH、LEACH-C、K-GA等算法,BBOK-GA降低了网络能耗,提高了网络吞吐量,延长了网络生存周期。
Clustering routing algorithm for WSN based on BBO optimized K-means
Aimed at the problems of limited energy and short network lifetime in wireless sensor network,BBOK-GA based on biogeographic algorithm optimization K-means was proposed.In the clustering stage,biogeographic algorithm optimization K-means was firstly used to prevent K-means from falling into the local optimum.According to the energy factor and distance factor,a new fitness function was designed to select optimal cluster heads and complete the clustering.And genetic algorithm was used to search the optimal routing path towards base station for cluster heads.The simulation results indicate that BBOK-GA reduces the network energy consumption,increases the network throughput and extends the network life time compared to LEACH,LEACH-C,and K-GA.

wireless sensor networkbiogeography-based optimizationgenetic algorithmK-means algorithmclustering-based routing

彭程、谭冲、刘洪、郑敏

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中国科学院大学,北京 100049

中国科学院上海微系统与信息技术研究所,上海 200050

无线传感器网络 生物地理学优化算法 遗传算法 K-means算法 分簇路由

国家重点研发计划

2020YFB2103300

2024

中国科学院大学学报
中国科学院大学

中国科学院大学学报

CSTPCD北大核心
影响因子:0.614
ISSN:2095-6134
年,卷(期):2024.41(3)
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