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Performance enhancement by efficient ant colony routing algorithm based on swarm intelligence in wireless sensor networks

机译:基于无线传感器网络中群智能的高效蚁群路由算法进行性能增强

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Enhanced Clustering Ant Colony Routing Modified (ECACR-M) algorithm is proposed in this paper which is the modified version of ECACR algorithm. Performance enhancement is the major issue in wireless networks, as source and destination are fixed so parameters like throughput, average delay and network latency can only be improved. Swarm intelligence inspired Ant Colony Optimisation (ACO) algorithms are popular to find optimum path. Standard Ant Colony Routing (SACR) algorithm introduced as first algorithm which was further improved and redefined as Improved Ant Colony Routing (IPACR) algorithm, but they take longer path. Improved Clustering Ant Colony Routing (1CACR) algorithm is developed on clustering technique and is better than IPACR algorithm by parameters like throughput and average delay, but not successful for case where sink node and source node are closest. The paper proposes modified algorithm ECACR-M that is motivated by ACO and clustering technique to find optimum route.
机译:本文提出了增强的聚类蚁群路由修改(ECACR-M)算法,这是ECACR算法的修改版本。性能增强是无线网络中的主要问题,因为源和目的地是固定的,所以只能提高像吞吐量,平均延迟和网络延迟的参数。群体智能启发蚂蚁殖民地优化(ACO)算法是寻找最佳路径的流行。标准蚁群路由(SAR)作为第一算法引入的算法,其进一步改进和重新定义为改进的蚁群路由(IPACR)算法,但它们需要更长的路径。改进的聚类蚁群路由(1CACR)算法是在聚类技术上开发的,并且比IPACR算法更好地参数,如吞吐量和平均延迟,但对于宿节点和源节点最接近的情况下没有成功。本文提出了由ACO和聚类技术激励的修改算法ECACR-M,以找到最佳路线。

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