Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Revealing the Retail Black Box by Interaction Sensing
ICDCSW '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Genetic Approach for Network Planning in the RFID Systems
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Application of A Multi-Species Optimizer in Ubiquitous Computing for RFID Networks Scheduling
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Load balancing in large-scale RFID systems
Computer Networks: The International Journal of Computer and Telecommunications Networking
Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab
Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab
Collaborative sensing in a retail store using synchronous distributed jam signalling
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Optimization based on bacterial chemotaxis
IEEE Transactions on Evolutionary Computation
RFID network planning using a multi-swarm optimizer
Journal of Network and Computer Applications
International Journal of Innovative Computing and Applications
RFID networks planning using BF-PSO
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
A multidimensional scaling localisation algorithm based on bacterial foraging optimisation
International Journal of Wireless and Mobile Computing
A fuzzy k-coverage approach for RFID network planning using plant growth simulation algorithm
Journal of Network and Computer Applications
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In order to obtain accurate and reliable network planning in the Radio Frequency Identification (RFID) communication system, the locations of readers and the associated values for each of the reader parameters have to be determined. All these choices must optimize a set of objectives, such as tag coverage, economic efficiency, load balance, and interference level between readers. This paper proposes a novel optimization algorithm, namely the multi-colony bacteria foraging optimization (MC-BFO), to solve complex RFID network planning problem. The main idea of MC-BFO is to extend the single population bacterial foraging algorithm to the interacting multi-colony model by relating the chemotactic behavior of single bacterial cell to the cell-to-cell communication of bacterial community. With this multi-colony cooperative approach, a suitable diversity in the whole bacterial community can be maintained. At the same time, the cell-to-cell communication mechanism significantly speeds up the bacterial community to converge to the global optimum. Then a mathematical model for planning RFID networks is developed based on the proposed MC-BFO. The performance of MC-BFO is compared to both GA and PSO on RFID network planning problem, demonstrating its superiority.