RFID networks planning using BF-PSO

Authors:
Qiwei Gu;Kai Yin;Ben Niu;Hanning Chen
Affiliations:
College of Management, Shenzhen, China;College of Management, Shenzhen, China;College of Management, Shenzhen, China,Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China,Institute for Cultural Industries, Shenzhen University, Shenzhen, China;Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, China
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

Citing 5
Cited 0

A RFID Network Planning Method Based on Genetic Algorithm

NSWCTC '09 Proceedings of the 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing - Volume 01
Original research paper: Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning

Applied Soft Computing
Symbiotic multi-swarm PSO for portfolio optimization

ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Bacterial foraging oriented by particle swarm optimization strategy for PID tuning

CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Multi-objective optimization using BFO algorithm

ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

RFID network planning (RNP) problem is a core challenge in the widespread application of RFID networks. In this paper, we develop a new mathematical model for planning RFID networks with multiple objectives, including carbon emissions, economic efficiency, load balance, interference between readers and coverage. Bacteria Foraging oriented by Particle Swarm Optimization (BF-PSO) is to solve optimize the proposed model. To demonstrate the effectiveness and efficiency of BF-PSO, the simulation results are compared with Bacteria Foraging Optimization (BFO), a real-coded Genetic Algorithm (RGA) and the Self-adaptive ES (SA-ES) on the RFID network planning problem.