基于网络覆盖和多目标离散群集蜘蛛算法的多移动agent规划

刘洲洲; 李士宁

doi:10.11959/j.issn.1000-436x.2017124

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基于网络覆盖和多目标离散群集蜘蛛算法的多移动agent规划

学术论文 | 更新时间：2024-06-05

- 基于网络覆盖和多目标离散群集蜘蛛算法的多移动agent规划
- Multi mobile agent itinerary planning based on network coverage and multi-objective discrete social spider optimization algorithm
- 通信学报 2017年38卷第6期页码：1-9
- 作者机构：
  
  1. 西安航空学院电子工程学院，陕西西安 710077
  2. 西北工业大学计算机学院，陕西西安 710072
- 作者简介：
  
  [ "刘洲洲（1981-），男，陕西延安人，西北工业大学博士后，西安航空学院副教授，主要研究方向为无线传感器网络、智能优化算法和不确定性推理。" ]
  [ "李士宁（1967-），男，陕西延安人，博士，西北工业大学教授、博士生导师，主要研究方向为智能计算、无线传感器网络。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61601365);陕西省教育厅科研计划基金资助项目(16JK1395)
- DOI：10.11959/j.issn.1000-436x.2017124
  中图分类号： TP393
- 网络出版日期：2017-06，
  
  纸质出版日期：2017-06-25
- 稿件说明：
移动端阅览
刘洲洲, 李士宁. 基于网络覆盖和多目标离散群集蜘蛛算法的多移动agent规划[J]. 通信学报, 2017,38(6):1-9.

Zhou-zhou LIU, Shi-ning LI. Multi mobile agent itinerary planning based on network coverage and multi-objective discrete social spider optimization algorithm[J]. Journal on communications, 2017, 38(6): 1-9.
刘洲洲, 李士宁. 基于网络覆盖和多目标离散群集蜘蛛算法的多移动agent规划[J]. 通信学报, 2017,38(6):1-9. DOI： 10.11959/j.issn.1000-436x.2017124.

Zhou-zhou LIU, Shi-ning LI. Multi mobile agent itinerary planning based on network coverage and multi-objective discrete social spider optimization algorithm[J]. Journal on communications, 2017, 38(6): 1-9. DOI： 10.11959/j.issn.1000-436x.2017124.

摘要

以agent负载能耗均衡度和网络总能耗为指标构建多移动agent协作规划模型，为了尽可能延长网络生存周期，给出基于网络覆盖率的节点休眠机制，在满足WSN网络覆盖率要求的同时，采用较少节点处于工作状态。根据多移动agent协作规划技术特点，设计融合Pareto最优解多目标离散群集蜘蛛算法（MDSSO），重新定义插值学习和变异交换粒子更新策略，并动态调整最优解集规模，以提高MDSSO算法多目标求解精度。实验仿真结果表明，该方法能够快速合理给出 WSN 多移动 agent 规划路径，而且与其他传统算法相比，网络总能耗降低了约15%，生存期提高了约23%。

Abstract

The multi mobile agent collaboration planning model was constructed based on the mobile agent load balancing and total network energy consumption index.In order to prolong the network lifetime

the network node dormancy mechanism based on WSN network coverage was put forward

using fewer worked nodes to meet the requirements of network coverage.According to the multi mobile agent collaborative planning technical features

the multi-objective discrete social spider optimization algorithm (MDSSO) with Pareto optimal solutions was designed.The interpolation learning and exchange variations particle updating strategy was redefined

and the optimal set size was adjusted dynamically

which helps to improve the accuracy of MDSSO.Simulation results show that the proposed algorithm can quickly give the WSN multi mobile agent path planning scheme

and compared with other schemes

the network total energy consumption has reduced by 15%

and the network lifetime has increased by 23%.

关键词

Keywords

references

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