Research On RFID System Optimization Models And Intelligent Algorithms Based On Biological Behaviors

Under the background of national strategy-Digital Convergence and Sensing China, Internet of things has received great attention from the government, industry, stock, and ect. RFID technology, as the main driving technology of the Internet of things, is considered one of the top ten important technologies in this century. At present, the applications of RFID system form a small scale market in the logistics, transportation, retail, and etc. However, the automation, intelligence, and coordination of the RFID system are still in low level. There are a lot of key problems of foundamental application technical researches to resolve. Among them, the RFID system optimization technique, as the basis to enssure the RFID system stable, reliable and safe operation, has become important issues in the RFID technology research and application.Due to the RFID system optimization generally being nonlinear, multi-objective, and large-scale complex problems, this thesis uses the characteristics that the intelligent optimization algorithms have more advantages than the traditional mathematical optimization algotithms in accuracy, convergence, initial value sensitivity, robustness and adaptability of solutions, and ect., for solving this kind of problems. On basis of the review of biological heuristic calculation researches, RFID system optimization models and intelligent altorithms based on biological behaviors are proposed. Through in-depth studing general and extensible RFID system optimization model, and designing a set of efficient and reliable intelligent optimization algorithms based on biological behaviors, the research fruits can mainly solve the optimization problems related to RFID reader scheduling, network loading balance, labels coverage, and multi-readers' data fusion, and ect., which improve the operation efficiency and service quality of the RFID system.The main research contents include following four aspects:1. The RFID reader anti-collision problems are studied by Symbiotic Multi-Species Particle Swarm Optimization(SMPSO). Through analying the RFID reader collision and conflicting models, a RFID reader anti-collision optimization model is studied, which considers minimizing reader conflicting and total processing time. Based on the standard particle swarm algorithm (PSO), the SMPSO algorithm, on account of biological symbiosis theory of the nature, is presented. By defining the information communication mechanism of cooperation in single species and collaboration between species, symbiotic strategy in ecological system is established, which has better diversity keeping ability and later searching performance. Usillizing simulation experiment that applies the SMPSO and PSO to four different sizes of RFID reader networks, the simulation result is showed that SMPSO's convergence speed and solutions are superior to that of PSO through comparing evolutionary curve during the iterative process. The Simulation experiment result shows that the SMPSO algorithm can effectively solve reader collision problem in intensive reader environment, and optimize the efficiency of the whole reader network.2. A clustering method is put forward based on Multi-Species Cooperative Artificial Bee Colony(MCABC) optimizaiton algorithm, that is applied in data analysis and processing of the RFID products in process tracking system. Taking advantage of multi-species co-evolutionary model, MCABC algorithm is proposed. A simulation experiment is tested on three standard clustering data set by MCABC, Artificial Bee Colony(ABC), PSO, and Cooperative Particle Swarm Optimization(CPSO). The simulation result shows that the convergence speed, solution accuracy and robustness of MCABC are superior to the other algorithms. The MCABC clustering algorithm is applied to the data processing module of the RFID products in process tracking system, then the data processing optimization model for the tracking system based on MCABC is propose. The simulation result shows that the model can effectively identify and eliminate errors, that may imporve the locating and tracking target accuracy of the RFID reader networks.3. The RFID network planning based on Hierarchical Bacterial Foraging Optimization (HBFO) is researched. A general and extensible RFID system optimization model is presented, then the HBFO algorithm based on heirarchy swarm intelligence optimization is designed, which is applied in the RFID network planning to solve large scale, multi-objective complex multi-peak problems. By the simulation experiment that HBFO, Genetic algorithm(GA), and PSO is used to solve the RFID networds planning, the simulation result reveals that the HBFO algorithm is superior to the other two algorithm in single objective function of labels coverage, reader interference, network loading balance and overall objective function of network economic efficiency.4. The Self-Adaptive Bacterial Foraging Optimization (SABFO) algorithm and RFID network dynamic optimization model are designed. Utilizing adaptive searching strategy and quorum-sensing mechanism,introduced to basic Bacteria foraging optimization (BFO) model,SABFO algorithm is built. By simulation experiment that is tested by applying the SABFO, PSO, BFO,and GA based real-coding algorithms in a group of standard test functions, the simulation results show that the convergence speed and solution accuracy of the SABFO are improved by different degrees.According to the dynamic and uncertainty of the RFID system itself, the RFID network dynamic optimization model is established based on static RFID network planning model, A simulation experiment is set through...