Cooperative artificial bee colony algorithm for multi-objective RFID network planning

Radio frequency identification (RFID) is rapidly growing into an important technology for object identification and tracking applications. This gives rise to the most challenging RFID network planning (RNP) problem in the large-scale RFID deployment environment. RNP has been proven to be an NP-hard problem that involves many objectives and constraints. The application of evolutionary and swarm intelligence algorithms for solving multi-objective RNP (MORNP) has gained significant attention in the literature, while these proposed methods always transform multi-objective RNP into single-objective problem by the weighted coefficient approach. In this work, we propose a cooperative multi-objective artificial colony algorithm called CMOABC to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP. The experiment presents an exhaustive comparison of the proposed CMOABC and two successful multi-objective techniques, namely the recently developed multi-objective artificial bee colony algorithm (MOABC) and nondominated sorting genetic algorithm II (NSGA-II), on instances of different nature, namely the two-objective and three-objective MORNP in the large-scale RFID scenario. Simulation results show that CMOABC proves to be superior for planning RFID networks compared to NSGA-II and MOABC in terms of optimization accuracy and computation robustness.

[1]  Maurizio Rebaudengo,et al.  Probabilistic DCS: An RFID reader-to-reader anti-collision protocol , 2011, J. Netw. Comput. Appl..

[2]  D. Wolpert,et al.  No Free Lunch Theorems for Search , 1995 .

[3]  Daniel W. Engels,et al.  The reader collision problem , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[4]  Indrajit Bhattacharya,et al.  Optimal Placement of Readers in an RFID Network Using Particle Swarm Optimization , 2010 .

[5]  Sherali Zeadally,et al.  RFID technology, systems, and applications , 2011, J. Netw. Comput. Appl..

[6]  D. P. Kothari,et al.  Stochastic economic emission load dispatch , 1993 .

[7]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[8]  Yahui Yang,et al.  A RFID Network Planning Method Based on Genetic Algorithm , 2009, 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing.

[9]  Gerhard P. Hancke,et al.  Design of a secure distance-bounding channel for RFID , 2011, J. Netw. Comput. Appl..

[10]  M. Kamel,et al.  A Taxonomy of Cooperative Search Algorithms , 2005, Hybrid Metaheuristics.

[11]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[12]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[13]  Taher Niknam,et al.  Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index , 2012 .

[14]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[15]  Reza Ghaffarian,et al.  CCGA packages for space applications , 2006, Microelectron. Reliab..

[16]  Yunlong Zhu,et al.  RFID networks planning using a multi-swarm optimizer , 2009, CCDC 2009.

[17]  Yu Liu,et al.  Genetic Approach for Network Planning in the RFID Systems , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[18]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[19]  Xiaodong Li,et al.  Cooperatively Coevolving Particle Swarms for Large Scale Optimization , 2012, IEEE Transactions on Evolutionary Computation.

[20]  Ju-Jang Lee,et al.  RFID sensor deployment using differential evolution for indoor mobile robot localization , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[22]  Meie Shen,et al.  Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination , 2012, IEEE Transactions on Industrial Informatics.

[23]  Junjie Li,et al.  An artificial bee colony algorithm for locating the critical slip surface in slope stability analysis , 2013 .

[24]  Wen Yao,et al.  Leveraging complex event processing for smart hospitals using RFID , 2011, J. Netw. Comput. Appl..

[25]  S. N. Omkar,et al.  Applied Soft Computing Artificial Bee Colony (abc) for Multi-objective Design Optimization of Composite Structures , 2022 .

[26]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[27]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[28]  Seyed Hossein Hosseinian,et al.  Modified artificial bee colony algorithm based on fuzzy multi-objective technique for optimal power flow problem , 2013 .

[29]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[30]  Junjie Li,et al.  Artificial bee colony algorithm and pattern search hybridized for global optimization , 2013, Appl. Soft Comput..

[31]  Junjie Li,et al.  Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions , 2011, Inf. Sci..

[32]  Dan Simon,et al.  Biogeography-based optimization and the solution of the power flow problem , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[33]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.

[34]  Albert Levi,et al.  PUF-enhanced offline RFID security and privacy , 2012, J. Netw. Comput. Appl..

[35]  Reza Akbari,et al.  A multi-objective Artificial Bee Colony for optimizing multi-objective problems , 2010, 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).