期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
OCSO-CA:opposition based competitive swarm optimizer in energy efficient IoT clustering
1
作者 Arpita BISWAS Abhishek MAJUMDAR +1 位作者 Soumyabrata DAS Krishna Lal BAISHNAB 《Frontiers of Computer Science》 SCIE EI CSCD 2022年第1期77-87,共11页
With the advent of modern technologies,IoT has become an alluring field of research.Since IoT connects everything to the network and transmits big data frequently,it can face issues regarding a large amount of energy ... With the advent of modern technologies,IoT has become an alluring field of research.Since IoT connects everything to the network and transmits big data frequently,it can face issues regarding a large amount of energy loss.In this respect,this paper mainly focuses on reducing the energy loss problem and designing an energy-efficient data transfer scenario between IoT devices and clouds.Consequently,a layered architectural framework for IoT-cloud transmission has been proposed that endorses the improvement in energy efficiency,network lifetime and latency.Furthermore,an Opposition based Competitive Swarm Optimizer oriented clustering approach named OCSO-CA has been proposed to get the optimal set of clusters in the IoT device network.The proposed strategy will help in managing intra-cluster and inter-cluster data communications in an energy-efficient way.Also,a comparative analysis of the proposed approach with the state-of-the-art optimization algorithms for clustering has been performed. 展开更多
关键词 competitive swarm optimization cloud computing CLUSTERING IoT
原文传递
Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization
2
作者 Lingzhi Yi Renzhe Duan +3 位作者 Wang Li Yihao Wang Dake Zhang Bo Liu 《Energy and Power Engineering》 2021年第4期41-51,共11页
<div style="text-align:justify;"> In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics ... <div style="text-align:justify;"> In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm. </div> 展开更多
关键词 Freight Train Automatic Train Operation Dynamics Model competitive Multi-Objective Particle swarm Optimization Algorithm (CMOPSO) Multi-Objective Optimization
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部