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Mathematical Problems in Engineering - Volume20142014, Article ID961412, 13 pages -

Research Article

Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

College of Management, Shenzhen University, Shenzhen 518060, China

Received 10 January 2014; Accepted 2 March 2014; Published 1 April 2014

Academic Editor: ChangzhiWu

Copyright 2014 Hanning Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The development of radio frequency identification RFID technology generates the most challenging RFID network planning RNP problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm EA and swarm intelligence SI for solving multiobjective RNP MORNP has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm MOABC, the nondominated sorting genetic algorithm II NSGA-II, and the multiobjective particle swarm optimization MOPSO, on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.

Author: Hanning Chen,Yunlong Zhu,Lianbo Ma,and Ben Niu



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