| Summary: | 碩士 === 長庚大學 === 電機工程學系 === 101 === In recent years, radio frequency identification (RFID) technique has been widely used in many applications. The RFID system consists of two types of devices, tags and readers. The area for a reader to identify tags is called the interrogation zone. Due to the limited interrogation range of the communication between the reader and the tag, the deployment of minimum number of readers to cover all tags in the entire region is known as the RFID reader network planning (RNP) problem. In general, the RNP problem is a type of resource allocation problems, which is a combinational optimization problem. In this paper, we propose a traditional genetic algorithm (TGA) to solve the RNP problem but this TGA is computational-time-consuming and does not guarantee to cover all of tags in the entire region. To overcome this point, we propose an improved genetic algorithm (IGA) which involved Micro-GA, correction mechanism and space crossover to solve this RNP problem. We have tested the proposed IGA on several RNP problems and compare with a traditional genetic algorithm (TGA) and a particle swarm optimization (PSO) method by solving the same RNPs. The comparison results demonstrate that the proposed IGA outperforms the TGA and the PSO method.
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