Discrete multi-objective optimization of particle swarm optimizer algorithm for multi-agents collaborative planning

Xiao-bo SHI; Yin ZHANG; Shan ZHAO; Deng-ming XIAO

doi:10.11959/j.issn.1000-436x.2016113

您当前的位置：

首页 >

文章列表页 >

Discrete multi-objective optimization of particle swarm optimizer algorithm for multi-agents collaborative planning

Papers | 更新时间：2024-06-05

- Discrete multi-objective optimization of particle swarm optimizer algorithm for multi-agents collaborative planning
- Journal on Communications Vol. 37, Issue 6, Pages: 29-37(2016)
- 作者机构：
  
  1. 河南师范大学计算机与信息工程学院，河南新乡 453007
  2. 华中科技大学计算机科学与技术学院，湖北武汉 430074
  3. 智慧商务与物联网技术河南省工程实验室，河南新乡 453007
  4. 中南财经政法大学信息与安全工程学院，湖北武汉 430073
- 作者简介：
- 基金信息：
  
  Key Science and Technology Program of Henan Province(132102210483);Key Science and Technology Program of Henan Province(102102210178);The Foundation and Cutting-edge Technologies Research Program of Henan Province(122300410344);Natural Science Research Projects of Department of Education of Henan Province(2008A520013)
- DOI：10.11959/j.issn.1000-436x.2016113
  CLC： TP393
- Online First：2016-06，
  
  Published：25 June 2016
- 稿件说明：
移动端阅览
Xiao-bo SHI, Yin ZHANG, Shan ZHAO, et al. Discrete multi-objective optimization of particle swarm optimizer algorithm for multi-agents collaborative planning[J]. Journal on Communications, 2016, 37(6): 29-37.
DOI：

Xiao-bo SHI, Yin ZHANG, Shan ZHAO, et al. Discrete multi-objective optimization of particle swarm optimizer algorithm for multi-agents collaborative planning[J]. Journal on Communications, 2016, 37(6): 29-37. DOI： 10.11959/j.issn.1000-436x.2016113.

摘要

无线传感器网络中多移动代理协作能快速高效地完成感知数据汇聚任务，但是随着移动代理访问数据源节点数的增加，移动代理携带的数据分组会逐渐增大，导致传感器节点能量负载不均衡，部分数据源节点能耗过快，网络生存期缩短。目前，针对该问题所设计的能耗均衡算法，多以降低多移动代理总能耗为目标，却未充分考虑部分数据源节点能量消耗过快对网络生存期造成的影响。提出离散多目标优化粒子群算法，以网络的总能耗和移动代理负载均衡作为适应度函数，在多移动代理协作路径规划中寻求近似最优解。通过仿真实验验证，所提出的多移动代理协作路径规划，在网络总能耗和网络生存期方面的性能优于同类其他算法。

Abstract

Although multiple mobile agents(MA)collaboration can quickly and efficiently complete data aggregation in wireless sensor network

the MA carrying data packages extensively increase along with a raise in the number of data source nodes accessed by MA

which causes unbalanced energy load of sensor nodes

high energy consumption of partial source nodes

and shortened lifetime of networks.The existing related works mainly focus on the objective of decreasing total energy consumption of multiple MA

without considering that rapidly energy consumption of partial source nodes has a negative effect on networks lifetime.Therefore

discrete multi-objective optimization of particle swarm algorithm was proposed

which used the total network energy consumption and mobile agent load balancing as fitness function for the approximate optimal itinerary plan in multiple mobile agent collaboration.Furthermore

the simulation result of the proposed algorithm is better than the similar algorithm in total energy consumption and network lifetime.

关键词

Keywords

references

VARAKLIOTIS S , HAILES S , DENARIDI R , et al . UAV and cognitive radio technologies in the emergency services arena [J/OL ] . British Association of Public Safety Communications Officials , http://eprints.ucl.ac.uk http://eprints.ucl.ac.uk .

ZAJKOWSKI T , DUNAGAN S , EILERS J . Small UAS communications mission [C ] // Eleventh Biennial USDA Forest Service Remote Sensing Applications . Salt Lake City,UT , 2006 .

SALEEM F , MOEEN Y , BEHZAD M , et al . IDDR:Improved density controlled divide-and-rule scheme for energy efficient routing in wireless sensor networks [J ] . Procedia Computer Science , 2014 , 34 : 212 - 219 .

KONSTANTOPOULOS C , MPITZIOPOULOS A , GAVALAS D , et al . Effective determination of mobile agent itineraries for data aggregation on sensor networks [J ] . IEEE Transactions on Knowledge and Data Engineering , 2010 , 22 ( 12 ): 1679 - 1693 .

ABDULLA A E A A , FADLULLAH Z M , NISHIYAMA H , et al . An optimal data collection technique for improved utility in UAS-aided networks [C ] // INFOCOM 2014 . Toronto,Canada , 2014 : 736 - 744 .

苏金树 , 郭文忠 , 余朝龙 , 等 . 负载均衡感知的无线传感器网络容错分簇算法 [J ] . 计算机学报 , 2014 , 37 ( 2 ): 445 - 456 .

SU J S , GUO W Z , YU C L , et al . Fault-tolerance clustering algorithm with load-balance aware in wireless sensor network [J ] . Chinese Journal of Computers , 2014 , 37 ( 2 ): 445 - 456 .

CHEN M . Towards smart city:M2M communications with software agent intelligence [J ] . Multimedia Tools and Applications , 2013 , 67 ( 1 ): 167 - 178 .

CHEN M , GONZALEZ S , ZHANG Q , et al . Code-centric RFID systems based on software agent intelligence [J ] . IEEE Intelligent Systems , 2010 , 25 ( 2 ): 12 - 19 .

QI H R , WANG F Y . Optimal itinerary analysis for mobile agents in ad hoc wireless sensor networks [C ] // Proceedings of the IEEE , 2001 : 147 - 153 .

CHEN M , YANG L T , KWON T , et al . Itinerary planning for energy-efficient agent communications in wireless sensor networks [J ] . IEEE Transactions on Vehicular Technology , 2011 , 60 ( 7 ): 3290 - 3299 .

CHEN M , LEUNG V , MAO S.W , et al . Energy-efficient itinerary planning for mobile agents in wireless sensor networks [C ] // IEEE International Conference on Communications (ICC'09) . Dresden,Germany , 2009 : 1 - 5 .

CAI W , CHEN M , HARA T , et al . A genetic algorithm approach to multi-agent itinerary planning in wireless sensor networks [J ] . Mobile Networks and Applications , 2011 , 16 ( 6 ): 782 - 793 .

WANG J F , ZHANG Y , CHENG Z L , et al . EMIP:energy-efficient itinerary planning for multiple mobile agents in wireless sensor network [J/OL ] . Telecommunication Systems , http://eprints.ucl.ac.uk http://eprints.ucl.ac.uk .

GAVALAS D , MPITZIOPOULOS A , PANTZIOU G , et al . An approach for near-optimal distributed data fusion in wireless sensor networks [J ] . Wireless Networks , 2010 , 16 ( 5 ): 1407 - 1425 .

CHEN M , CAI W , GONZALEZ S , et al . Balanced itinerary planning for multiple mobile agents in wireless sensor networks [M ] . Ad Hoc Networks . Springer Berlin Heidelberg , 2010 : 416 - 428 .

HUANG V L , SUGANTHAN P N , LIANG J J . Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems [J ] . International Journal of Intelligent Systems , 2006 , 21 ( 2 ): 209 - 226 .

MAO W T , ZHAO S J , MU X X , et al . Multi-dimensional extreme learning machine [J ] . Neurocomputing , 2015 , 149 ( 4 ): 160 - 170 .

CLERC M . Discrete particle swarm optimization,illustrated by the traveling salesman problem [M ] . New optimization techniques in engineering . Springer Berlin Heidelberg , 2004 .

陈敏 . OPNET物联网仿真 [M ] . 武汉:华中科技大学出版社 . 2015 .

CHEN M . OPNET Internet of things simulation [M ] . Wuhan : Huazhong University of Science and Technology Press , 2015 .

Views

1301

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

WSN clustering routing algorithm based on PSO optimized fuzzy C-means

Multi mobile agent itinerary planning based on network coverage and multi-objective discrete social spider optimization algorithm

Multi-indicator intention-driven adaptive optimization strategy for UAV computation offloading and trajectory planning

GPS-independent lightweight reflective collaborative false data filtering scheme for sensor networks

Anomaly detection method for spatio-temporal correlation of WSN nodes based on graph prompt fine-tuning

Related Author

Aijing SUN

Shichang LI

Yicai ZHANG

Zhou-zhou LIU

Shi-ning LI

Lin Peng

Huang Xinliang

Ning Zhaolong

Related Institution

School of Communication and Information Engineering, Xi’an University of Posts ＆Telecommunications

School of Electronic Engineering, Xi’an University of Posts ＆Telecommunications

School of Electronic Engineering,Xi’an Aeronautical University

School of Computer Science,Northwestern Polytechnical University

Key Laboratory of Intelligent Support Technology for Complex Environments, Ministry of Education, Nanjing University of Information Science and Technology

AI问答

⁰